AIM: To investigate whether a fuzzy logic model could predict colorectal cancer (CRC) risk engendered by smoking in hereditary non-polyposis colorectal cancer (HNPCC) patients. METHODS: Three hundred and forty H...AIM: To investigate whether a fuzzy logic model could predict colorectal cancer (CRC) risk engendered by smoking in hereditary non-polyposis colorectal cancer (HNPCC) patients. METHODS: Three hundred and forty HNPCC mismatch repair (MMR) mutation carriers from the Creighton University Hereditary Cancer Institute Registry were selected for modeling. Age-dependent curves were generated to elucidate the joint effects between gene mutation (hMLH1 or hMSH2), gender, and smoking status on the probability of developing CRC. RESULTS: Smoking significantly increased CRC risk in male hMSH2 mutation carriers (P 〈 0.05). hMLH1 mutations augmented CRC risk relative to hMSH2 mutation carriers for males (P 〈 0.05). Males had a significantly higher risk of CRC than females for hMLH1 non smokers (P 〈 0.05), hMLH1 smokers (P 〈 0.1) and hMSH2 smokers (P 〈 0.1). Smoking promoted CRC in a dose-dependent manner in hMSH2 in males (P 〈 0.05). Females with hMSH2 mutations and both sexes with the hMLH1 groups only demonstrated a smoking effect after an extensive smoking history (P 〈 0.05). CONCLUSION: CRC promotion by smoking in HNPCC patients is dependent on gene mutation, gender and age. These data demonstrate that fuzzy modeling may enable formulation of clinical risk scores, thereby allowing individualization of CRC prevention strategies.展开更多
A method of fuzzy modeling based on fuzzy clustering and Kalman filtering was proposed for predicting M s temperature from chemical composition for martensitic stainless steel. The membership degree of each sample wa...A method of fuzzy modeling based on fuzzy clustering and Kalman filtering was proposed for predicting M s temperature from chemical composition for martensitic stainless steel. The membership degree of each sample was calculated by the fuzzy clustering algorithm. Kalman filtering was used to identify the consequent parameters. Only Grade 95 steel are available for training and validation, and the fuzzy model is valid for the following element concentration ranges (wt%): 0.01<C<0.7; 0<Si<1.0; 0.10<Mn<1.25; 11.5<Cr< 17.5; 0<Ni<2.5; 0<Mo<1.0. Compared with that of several empirical models reported, the accuracy of the fuzzy model was almost 5 times higher than that of the best empirical model. Furthermore, the compositional dependences of Ms were successfully determined and compared with those of the empirical formulae. It was found that the specific element dependences were a function of the overall composition, something could not easily be found using conventional statistics.展开更多
In this paper, a novel method to model, track control and synchronize the Rossler’s chaotic system is proposed. The fuzzy logical system is used so that the fuzzy inference rule is transferred into a type of variable...In this paper, a novel method to model, track control and synchronize the Rossler’s chaotic system is proposed. The fuzzy logical system is used so that the fuzzy inference rule is transferred into a type of variable coefficient nonlinear ordinary differential equation. Consequently the model of the chaotic system is obtained. Then a fuzzy tracking control and a fuzzy synchronization for chaotic systems is proposed as well. First, a known tracking control for the Rossler’s system is used in this paper. We represent the Rossler’s chaotic and control systems into fuzzy inference rules. Then the variable coefficient nonlinear ordinary differential equation is also got. Simulation results show that such an approach is effective and has a high precision.展开更多
High level of industrialization of northern regions of Russia cause significant pollution problems in the soil. The new approach based on fuzzy modeling for reception and processing of the geocryological information i...High level of industrialization of northern regions of Russia cause significant pollution problems in the soil. The new approach based on fuzzy modeling for reception and processing of the geocryological information is offered. The technique and methodology of presentation of variables in a logic and linguistic way in a combination to elements of the experimental planning theory is developed. Comparison of the calculated data on model has shown its high adequacy of the experimental data of the various authors. The forecast of geoecological processes in cryolitozone on the basis of the described method will allow increasing reliability of the accepted design decisions.展开更多
Based on the multi-model principle, the fuzzy identification for nonlinear systems with multirate sampled data is studied.Firstly, the nonlinear system with multirate sampled data can be shown as the nonlinear weighte...Based on the multi-model principle, the fuzzy identification for nonlinear systems with multirate sampled data is studied.Firstly, the nonlinear system with multirate sampled data can be shown as the nonlinear weighted combination of some linear models at multiple local working points. On this basis, the fuzzy model of the multirate sampled nonlinear system is built. The premise structure of the fuzzy model is confirmed by using fuzzy competitive learning, and the conclusion parameters of the fuzzy model are estimated by the random gradient descent algorithm. The convergence of the proposed identification algorithm is given by using the martingale theorem and lemmas. The fuzzy model of the PH neutralization process of acid-base titration for hair quality detection is constructed to demonstrate the effectiveness of the proposed method.展开更多
A fuzzy modeling method for complex systems is studied. The notation of general stochastic neural network (GSNN) is presented and a new modeling method is given based on the combination of the modified Takagi and Suge...A fuzzy modeling method for complex systems is studied. The notation of general stochastic neural network (GSNN) is presented and a new modeling method is given based on the combination of the modified Takagi and Sugeno's (MTS) fuzzy model and one-order GSNN. Using expectation-maximization(EM) algorithm, parameter estimation and model selection procedures are given. It avoids the shortcomings brought by other methods such as BP algorithm, when the number of parameters is large, BP algorithm is still difficult to apply directly without fine tuning and subjective tinkering. Finally, the simulated example demonstrates the effectiveness.展开更多
The studying motivation of this paper is that there exist many modeling issues of nonuniformly sampling nonlinear systems in industrial systems.Based on multi-model modeling principle,the corresponding model of non-un...The studying motivation of this paper is that there exist many modeling issues of nonuniformly sampling nonlinear systems in industrial systems.Based on multi-model modeling principle,the corresponding model of non-uniformly sampling nonlinear systems is described by the nonlinear weighted combination of some linear models at local working points.Fuzzy modeling based on multimodel scheme is a common method to describe the dynamic process of non-linear systems.In this paper,the fuzzy modeling method of non-uniformly sampling nonlinear systems is studied.The premise structure of the fuzzy model is confirmed by GK fuzzy clustering,and the conclusion parameters of the fuzzy model are estimated by the recursive least squared algorithm.The convergence perfromance of the proposed identification algorithm is given by using lemmas and martingale theorem.Finally,the simulation example is given to demonstrate the effectiveness of the proposed method.展开更多
A multilayer recurrent fuzzy neural network(MRFNN)is proposed for accurate dynamic system modeling.The proposed MRFNN has six layers combined with T-S fuzzy model.The recurrent structures are formed by local feedback ...A multilayer recurrent fuzzy neural network(MRFNN)is proposed for accurate dynamic system modeling.The proposed MRFNN has six layers combined with T-S fuzzy model.The recurrent structures are formed by local feedback connections in the membership layer and the rule layer.With these feedbacks,the fuzzy sets are time-varying and the temporal problem of dynamic system can be solved well.The parameters of MRFNN are learned by chaotic search(CS)and least square estimation(LSE)simultaneously,where CS is for tuning the premise parameters and LSE is for updating the consequent coefficients accordingly.Results of simulations show the proposed approach is effective for dynamic system modeling with high accuracy.展开更多
The capability requirements of the command, control, communication, computing, intelligence, surveillance, reconnaissance (C41SR) systems are full of uncertain and vague information, which makes it difficult to mode...The capability requirements of the command, control, communication, computing, intelligence, surveillance, reconnaissance (C41SR) systems are full of uncertain and vague information, which makes it difficult to model the C41SR architecture. The paper presents an approach to modeling the capability requirements with the fuzzy unified modeling language (UML) and building domain ontologies with fuzzy description logic (DL). The UML modeling constructs are extended according to the meta model of Depart- ment of Defense Architecture Framework to improve their domain applicability, the fuzzy modeling mechanism is introduced to model the fuzzy efficiency features of capabilities, and the capability requirement models are converted into ontologies formalized in fuzzy DL so that the model consistency and reasonability can be checked with a DL reasoning system. Finally, a case study of C41SR capability requirements model checking is provided to demonstrate the availability and applicability of the method.展开更多
Diabetes mellitus is associated with foot ulcers,which frequently pave the way to lower-extremity amputation.Neuropathy,trauma,deformity,high plantar pressures,and peripheral vascular disease are the most common under...Diabetes mellitus is associated with foot ulcers,which frequently pave the way to lower-extremity amputation.Neuropathy,trauma,deformity,high plantar pressures,and peripheral vascular disease are the most common underlying causes.Around 15%of diabetic patients are affected by diabetic foot ulcer in their lifetime.64 million people are affected by diabetics in India and 40000 amputations are done every year.Foot ulcers are evaluated and classified in a systematic and thorough manner to assist in determining the best course of therapy.This paper proposes a novel model which predicts the threat of diabetic foot ulcer using independent agents for various input values and a combination of fuzzy expert systems.The proposed model uses a classification system to distinguish between each fuzzy framework and its parameters.Based on the severity levels necessary prevention,treatment,and medication are recommended.Combining the results of all the fuzzy frameworks derived from its constituent parameters,a risk-specific medication is recommended.The work also has higher accuracy when compared to other related models.展开更多
In many Eastern and Western countries,falling birth rates have led to the gradual aging of society.Older adults are often left alone at home or live in a long-term care center,which results in them being susceptible t...In many Eastern and Western countries,falling birth rates have led to the gradual aging of society.Older adults are often left alone at home or live in a long-term care center,which results in them being susceptible to unsafe events(such as falls)that can have disastrous consequences.However,automatically detecting falls fromvideo data is challenging,and automatic fall detection methods usually require large volumes of training data,which can be difficult to acquire.To address this problem,video kinematic data can be used as training data,thereby avoiding the requirement of creating a large fall data set.This study integrated an improved particle swarm optimization method into a double interactively recurrent fuzzy cerebellar model articulation controller model to develop a costeffective and accurate fall detection system.First,it obtained an optical flow(OF)trajectory diagram from image sequences by using the OF method,and it solved problems related to focal length and object offset by employing the discrete Fourier transform(DFT)algorithm.Second,this study developed the D-IRFCMAC model,which combines spatial and temporal(recurrent)information.Third,it designed an IPSO(Improved Particle Swarm Optimization)algorithm that effectively strengthens the exploratory capabilities of the proposed D-IRFCMAC(Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller)model in the global search space.The proposed approach outperforms existing state-of-the-art methods in terms of action recognition accuracy on the UR-Fall,UP-Fall,and PRECIS HAR data sets.The UCF11 dataset had an average accuracy of 93.13%,whereas the UCF101 dataset had an average accuracy of 92.19%.The UR-Fall dataset had an accuracy of 100%,the UP-Fall dataset had an accuracy of 99.25%,and the PRECIS HAR dataset had an accuracy of 99.07%.展开更多
This paper addresses the problem of the fuzzy H ∞state feedback control for a class of uncertain nonlinear systems with time delay. The Takagi Sugeno (T S) mo del with time delay and parameter uncertainties is ...This paper addresses the problem of the fuzzy H ∞state feedback control for a class of uncertain nonlinear systems with time delay. The Takagi Sugeno (T S) mo del with time delay and parameter uncertainties is adopted for modeling of nonlinear system. The systematic design procedure for the fuzzy robust controller based on linear matrix inequality (LMI) is given. Some sufficient conditions are derived for the existence of fuzzy H ∞ state feedback controllers such that the closed loop system is asymptotically stable and the effect of the disturbance input on controlled output is reduced to a prescribed level. An example is given to demonstrate the effectiveness of the proposed method.展开更多
In this article,the problem of state estimation is addressed for discrete-time nonlinear systems subject to additive unknown-but-bounded noises by using fuzzy set-membership filtering.First,an improved T-S fuzzy model...In this article,the problem of state estimation is addressed for discrete-time nonlinear systems subject to additive unknown-but-bounded noises by using fuzzy set-membership filtering.First,an improved T-S fuzzy model is introduced to achieve highly accurate approximation via an affine model under each fuzzy rule.Then,compared to traditional prediction-based ones,two types of fuzzy set-membership filters are proposed to effectively improve filtering performance,where the structure of both filters consists of two parts:prediction and filtering.Under the locally Lipschitz continuous condition of membership functions,unknown membership values in the estimation error system can be treated as multiplicative noises with respect to the estimation error.Real-time recursive algorithms are given to find the minimal ellipsoid containing the true state.Finally,the proposed optimization approaches are validated via numerical simulations of a one-dimensional and a three-dimensional discrete-time nonlinear systems.展开更多
An adaptive neuro-fuzzy inference system(ANFIS) for predicting the performance of a reversibly used cooling tower(RUCT) under cross flow conditions as part of a heat pump system for a heating mode in winter was demons...An adaptive neuro-fuzzy inference system(ANFIS) for predicting the performance of a reversibly used cooling tower(RUCT) under cross flow conditions as part of a heat pump system for a heating mode in winter was demonstrated.Extensive field experimental work was carried out in order to gather enough data for training and prediction.The statistical methods,such as the correlation coefficient,absolute fraction of variance and root mean square error,were given to compare the predicted and actual values for model validation.The simulation results predicted with the ANFIS can be used to simulate the performance of a reversibly used cooling tower quite accurately.Therefore,the ANFIS approach can reliably be used for forecasting the performance of RUCT.展开更多
Input variables selection(IVS) is proved to be pivotal in nonlinear dynamic system modeling. In order to optimize the model of the nonlinear dynamic system, a fuzzy modeling method for determining the premise structur...Input variables selection(IVS) is proved to be pivotal in nonlinear dynamic system modeling. In order to optimize the model of the nonlinear dynamic system, a fuzzy modeling method for determining the premise structure by selecting important inputs of the system is studied. Firstly, a simplified two stage fuzzy curves method is proposed, which is employed to sort all possible inputs by their relevance with outputs, select the important input variables of the system and identify the structure.Secondly, in order to reduce the complexity of the model, the standard fuzzy c-means clustering algorithm and the recursive least squares algorithm are used to identify the premise parameters and conclusion parameters, respectively. Then, the effectiveness of IVS is verified by two well-known issues. Finally, the proposed identification method is applied to a realistic variable load pneumatic system. The simulation experiments indi cate that the IVS method in this paper has a positive influence on the approximation performance of the Takagi-Sugeno(T-S) fuzzy modeling.展开更多
This paper presents a fuzzy logic approach to efficiently perform unsupervised character classification for improvement in robustness, correctness and speed of a character recognition system. The characters are first ...This paper presents a fuzzy logic approach to efficiently perform unsupervised character classification for improvement in robustness, correctness and speed of a character recognition system. The characters are first split into eight typographical categories. The classification scheme uses pattern matching to classify the characters in each category into a set of fuzzy prototypes based on a nonlinear weighted similarity function. The fuzzy unsupervised character classification, which is natural in the repre...展开更多
In order to overcome data-quantization, networked-induced delay, network packet dropouts and wrong sequences in the nonlinear networked control system, a novel nonlinear networked control system model is built by the ...In order to overcome data-quantization, networked-induced delay, network packet dropouts and wrong sequences in the nonlinear networked control system, a novel nonlinear networked control system model is built by the T-S fuzzy method. Two time-varying quantizers are added in the model. The key analysis steps in the method are to construct an improved interval-delay-dependent Lyapunov functional and to introduce the free-weighting matrix. By making use of the parallel distributed compensation technology and the convexity of the matrix function, the improved criteria of the stabilization and stability are obtained. Simulation experiments show that the parameters of the controllers and quantizers satisfying a certain performance can be obtained by solving a set of LMIs. The application of the nonlinear mass-spring system is provided to show that the proposed method is effective.展开更多
Tourism demand forecasting has attracted substantial interest because of the significant economic contributions of the fast-growing tourism industry. Although various quantitative forecasting techniques have been wide...Tourism demand forecasting has attracted substantial interest because of the significant economic contributions of the fast-growing tourism industry. Although various quantitative forecasting techniques have been widely studied, highly accurate and understandable forecasting models have not been developed. The present paper proposes a novel tourism demand forecasting method that extracts fuzzy Takagi-Sugeno (T-S) rules from trained SVMs. Unlike previous approaches, this study uses fuzzy T-S models extracted from the outputs of trained SVMs on tourism data. Owing to the symbolic fuzzy rules and the generalization ability of SVMs, the extracted fuzzy T-S rules exhibit high forecasting accuracy and include understandable pre-condition parts for practitioners. Based on the tourism demand forecasting problem in Hong Kong SAR, China as a case study, empirical findings on tourist arrivals from nine overseas origins reveal that the proposed approach performs comparably with SVMs and can achieve better prediction accuracy than other forecasting techniques for most origins. The findings demonstrated that decision makers can easily interpret fuzzy T-S rules extracted from SVMs. Thus, the approach is highly beneficial to tourism market management. This finding demonstrates the excellent scientific and practical values of the proposed approach in tourism demand forecasting.展开更多
A novel multiresolution pyramidal edge detector, based on adaptive weighted fuzzy mean(AWFM)filtering and fuzzy linking model, is presented in this paper. The algorithm first constructs a pyramidal structure by repeti...A novel multiresolution pyramidal edge detector, based on adaptive weighted fuzzy mean(AWFM)filtering and fuzzy linking model, is presented in this paper. The algorithm first constructs a pyramidal structure by repetitive AWFM filtering and subsampling of original image. Then it utilizes multiple heuristic linking criteria between the edge nodes of two adjacent levels and considers the linkage as a fuzzy model, which is trained offline. Through this fuzzy linking model, the boundaries detected at coarse resolution are propagated and refined to the bottom level from the coarse-to fine edge detection. The validation experiment results demonstrate that the proposed approach has superior performance compared with standard fixed resolution detector andprevious multiresolution approach, especially in impulse noise environment.展开更多
This paper deals with the problem of stabilization design for a class of continuous-time Takagi-Sugeno(T-S)fuzzy systems.New stabilization conditions are derived based on a relaxed approach in which both fuzzy Lyapu...This paper deals with the problem of stabilization design for a class of continuous-time Takagi-Sugeno(T-S)fuzzy systems.New stabilization conditions are derived based on a relaxed approach in which both fuzzy Lyapunov functions and staircase membership functions are used.Through the staircase membership functions approximating the continuous membership functions of the given fuzzy model,the information of the membership functions can be brought into the stabilization design of the fuzzy systems,thereby significantly reducing the conservativeness in the existing stabilization conditions of the T-S fuzzy systems.Unlike some previous fuzzy Lyapunov function approaches reported in the literature,the proposed stabilization conditions do not depend on the time-derivative of the membership functions that may be the main source of conservatism when considering fuzzy Lyapunov functions analysis.Moreover,conditions for the solvability of the controller design are written in the form of linear matrix inequalities,but not bilinear matrix inequalities,which are easier to be solved by convex optimization techniques.A simulation example is given to demonstrate the validity of the proposed approach.展开更多
基金Supported by a grant from the American College of Gastroenterology
文摘AIM: To investigate whether a fuzzy logic model could predict colorectal cancer (CRC) risk engendered by smoking in hereditary non-polyposis colorectal cancer (HNPCC) patients. METHODS: Three hundred and forty HNPCC mismatch repair (MMR) mutation carriers from the Creighton University Hereditary Cancer Institute Registry were selected for modeling. Age-dependent curves were generated to elucidate the joint effects between gene mutation (hMLH1 or hMSH2), gender, and smoking status on the probability of developing CRC. RESULTS: Smoking significantly increased CRC risk in male hMSH2 mutation carriers (P 〈 0.05). hMLH1 mutations augmented CRC risk relative to hMSH2 mutation carriers for males (P 〈 0.05). Males had a significantly higher risk of CRC than females for hMLH1 non smokers (P 〈 0.05), hMLH1 smokers (P 〈 0.1) and hMSH2 smokers (P 〈 0.1). Smoking promoted CRC in a dose-dependent manner in hMSH2 in males (P 〈 0.05). Females with hMSH2 mutations and both sexes with the hMLH1 groups only demonstrated a smoking effect after an extensive smoking history (P 〈 0.05). CONCLUSION: CRC promotion by smoking in HNPCC patients is dependent on gene mutation, gender and age. These data demonstrate that fuzzy modeling may enable formulation of clinical risk scores, thereby allowing individualization of CRC prevention strategies.
文摘A method of fuzzy modeling based on fuzzy clustering and Kalman filtering was proposed for predicting M s temperature from chemical composition for martensitic stainless steel. The membership degree of each sample was calculated by the fuzzy clustering algorithm. Kalman filtering was used to identify the consequent parameters. Only Grade 95 steel are available for training and validation, and the fuzzy model is valid for the following element concentration ranges (wt%): 0.01<C<0.7; 0<Si<1.0; 0.10<Mn<1.25; 11.5<Cr< 17.5; 0<Ni<2.5; 0<Mo<1.0. Compared with that of several empirical models reported, the accuracy of the fuzzy model was almost 5 times higher than that of the best empirical model. Furthermore, the compositional dependences of Ms were successfully determined and compared with those of the empirical formulae. It was found that the specific element dependences were a function of the overall composition, something could not easily be found using conventional statistics.
文摘In this paper, a novel method to model, track control and synchronize the Rossler’s chaotic system is proposed. The fuzzy logical system is used so that the fuzzy inference rule is transferred into a type of variable coefficient nonlinear ordinary differential equation. Consequently the model of the chaotic system is obtained. Then a fuzzy tracking control and a fuzzy synchronization for chaotic systems is proposed as well. First, a known tracking control for the Rossler’s system is used in this paper. We represent the Rossler’s chaotic and control systems into fuzzy inference rules. Then the variable coefficient nonlinear ordinary differential equation is also got. Simulation results show that such an approach is effective and has a high precision.
文摘High level of industrialization of northern regions of Russia cause significant pollution problems in the soil. The new approach based on fuzzy modeling for reception and processing of the geocryological information is offered. The technique and methodology of presentation of variables in a logic and linguistic way in a combination to elements of the experimental planning theory is developed. Comparison of the calculated data on model has shown its high adequacy of the experimental data of the various authors. The forecast of geoecological processes in cryolitozone on the basis of the described method will allow increasing reliability of the accepted design decisions.
基金supported by the National Natural Science Foundation of China(61863034)。
文摘Based on the multi-model principle, the fuzzy identification for nonlinear systems with multirate sampled data is studied.Firstly, the nonlinear system with multirate sampled data can be shown as the nonlinear weighted combination of some linear models at multiple local working points. On this basis, the fuzzy model of the multirate sampled nonlinear system is built. The premise structure of the fuzzy model is confirmed by using fuzzy competitive learning, and the conclusion parameters of the fuzzy model are estimated by the random gradient descent algorithm. The convergence of the proposed identification algorithm is given by using the martingale theorem and lemmas. The fuzzy model of the PH neutralization process of acid-base titration for hair quality detection is constructed to demonstrate the effectiveness of the proposed method.
文摘A fuzzy modeling method for complex systems is studied. The notation of general stochastic neural network (GSNN) is presented and a new modeling method is given based on the combination of the modified Takagi and Sugeno's (MTS) fuzzy model and one-order GSNN. Using expectation-maximization(EM) algorithm, parameter estimation and model selection procedures are given. It avoids the shortcomings brought by other methods such as BP algorithm, when the number of parameters is large, BP algorithm is still difficult to apply directly without fine tuning and subjective tinkering. Finally, the simulated example demonstrates the effectiveness.
基金the National Natural Science Foundation of China under Grant Nos.61863034and 51667021。
文摘The studying motivation of this paper is that there exist many modeling issues of nonuniformly sampling nonlinear systems in industrial systems.Based on multi-model modeling principle,the corresponding model of non-uniformly sampling nonlinear systems is described by the nonlinear weighted combination of some linear models at local working points.Fuzzy modeling based on multimodel scheme is a common method to describe the dynamic process of non-linear systems.In this paper,the fuzzy modeling method of non-uniformly sampling nonlinear systems is studied.The premise structure of the fuzzy model is confirmed by GK fuzzy clustering,and the conclusion parameters of the fuzzy model are estimated by the recursive least squared algorithm.The convergence perfromance of the proposed identification algorithm is given by using lemmas and martingale theorem.Finally,the simulation example is given to demonstrate the effectiveness of the proposed method.
文摘A multilayer recurrent fuzzy neural network(MRFNN)is proposed for accurate dynamic system modeling.The proposed MRFNN has six layers combined with T-S fuzzy model.The recurrent structures are formed by local feedback connections in the membership layer and the rule layer.With these feedbacks,the fuzzy sets are time-varying and the temporal problem of dynamic system can be solved well.The parameters of MRFNN are learned by chaotic search(CS)and least square estimation(LSE)simultaneously,where CS is for tuning the premise parameters and LSE is for updating the consequent coefficients accordingly.Results of simulations show the proposed approach is effective for dynamic system modeling with high accuracy.
文摘The capability requirements of the command, control, communication, computing, intelligence, surveillance, reconnaissance (C41SR) systems are full of uncertain and vague information, which makes it difficult to model the C41SR architecture. The paper presents an approach to modeling the capability requirements with the fuzzy unified modeling language (UML) and building domain ontologies with fuzzy description logic (DL). The UML modeling constructs are extended according to the meta model of Depart- ment of Defense Architecture Framework to improve their domain applicability, the fuzzy modeling mechanism is introduced to model the fuzzy efficiency features of capabilities, and the capability requirement models are converted into ontologies formalized in fuzzy DL so that the model consistency and reasonability can be checked with a DL reasoning system. Finally, a case study of C41SR capability requirements model checking is provided to demonstrate the availability and applicability of the method.
文摘Diabetes mellitus is associated with foot ulcers,which frequently pave the way to lower-extremity amputation.Neuropathy,trauma,deformity,high plantar pressures,and peripheral vascular disease are the most common underlying causes.Around 15%of diabetic patients are affected by diabetic foot ulcer in their lifetime.64 million people are affected by diabetics in India and 40000 amputations are done every year.Foot ulcers are evaluated and classified in a systematic and thorough manner to assist in determining the best course of therapy.This paper proposes a novel model which predicts the threat of diabetic foot ulcer using independent agents for various input values and a combination of fuzzy expert systems.The proposed model uses a classification system to distinguish between each fuzzy framework and its parameters.Based on the severity levels necessary prevention,treatment,and medication are recommended.Combining the results of all the fuzzy frameworks derived from its constituent parameters,a risk-specific medication is recommended.The work also has higher accuracy when compared to other related models.
基金supported by the National Science and Technology Council under grants NSTC 112-2221-E-320-002the Buddhist Tzu Chi Medical Foundation in Taiwan under Grant TCMMP 112-02-02.
文摘In many Eastern and Western countries,falling birth rates have led to the gradual aging of society.Older adults are often left alone at home or live in a long-term care center,which results in them being susceptible to unsafe events(such as falls)that can have disastrous consequences.However,automatically detecting falls fromvideo data is challenging,and automatic fall detection methods usually require large volumes of training data,which can be difficult to acquire.To address this problem,video kinematic data can be used as training data,thereby avoiding the requirement of creating a large fall data set.This study integrated an improved particle swarm optimization method into a double interactively recurrent fuzzy cerebellar model articulation controller model to develop a costeffective and accurate fall detection system.First,it obtained an optical flow(OF)trajectory diagram from image sequences by using the OF method,and it solved problems related to focal length and object offset by employing the discrete Fourier transform(DFT)algorithm.Second,this study developed the D-IRFCMAC model,which combines spatial and temporal(recurrent)information.Third,it designed an IPSO(Improved Particle Swarm Optimization)algorithm that effectively strengthens the exploratory capabilities of the proposed D-IRFCMAC(Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller)model in the global search space.The proposed approach outperforms existing state-of-the-art methods in terms of action recognition accuracy on the UR-Fall,UP-Fall,and PRECIS HAR data sets.The UCF11 dataset had an average accuracy of 93.13%,whereas the UCF101 dataset had an average accuracy of 92.19%.The UR-Fall dataset had an accuracy of 100%,the UP-Fall dataset had an accuracy of 99.25%,and the PRECIS HAR dataset had an accuracy of 99.07%.
文摘This paper addresses the problem of the fuzzy H ∞state feedback control for a class of uncertain nonlinear systems with time delay. The Takagi Sugeno (T S) mo del with time delay and parameter uncertainties is adopted for modeling of nonlinear system. The systematic design procedure for the fuzzy robust controller based on linear matrix inequality (LMI) is given. Some sufficient conditions are derived for the existence of fuzzy H ∞ state feedback controllers such that the closed loop system is asymptotically stable and the effect of the disturbance input on controlled output is reduced to a prescribed level. An example is given to demonstrate the effectiveness of the proposed method.
基金supported in part by the National Natural Science Foundation of China(61973219,61933007,62073158)the China Scholarship Council(201908310148)。
文摘In this article,the problem of state estimation is addressed for discrete-time nonlinear systems subject to additive unknown-but-bounded noises by using fuzzy set-membership filtering.First,an improved T-S fuzzy model is introduced to achieve highly accurate approximation via an affine model under each fuzzy rule.Then,compared to traditional prediction-based ones,two types of fuzzy set-membership filters are proposed to effectively improve filtering performance,where the structure of both filters consists of two parts:prediction and filtering.Under the locally Lipschitz continuous condition of membership functions,unknown membership values in the estimation error system can be treated as multiplicative noises with respect to the estimation error.Real-time recursive algorithms are given to find the minimal ellipsoid containing the true state.Finally,the proposed optimization approaches are validated via numerical simulations of a one-dimensional and a three-dimensional discrete-time nonlinear systems.
基金Projects(51108165, 51178170) supported by the National Natural Science Foundation of China
文摘An adaptive neuro-fuzzy inference system(ANFIS) for predicting the performance of a reversibly used cooling tower(RUCT) under cross flow conditions as part of a heat pump system for a heating mode in winter was demonstrated.Extensive field experimental work was carried out in order to gather enough data for training and prediction.The statistical methods,such as the correlation coefficient,absolute fraction of variance and root mean square error,were given to compare the predicted and actual values for model validation.The simulation results predicted with the ANFIS can be used to simulate the performance of a reversibly used cooling tower quite accurately.Therefore,the ANFIS approach can reliably be used for forecasting the performance of RUCT.
基金This work was supported by the Natural Science Foundation of Hebei Province(F2019203505).
文摘Input variables selection(IVS) is proved to be pivotal in nonlinear dynamic system modeling. In order to optimize the model of the nonlinear dynamic system, a fuzzy modeling method for determining the premise structure by selecting important inputs of the system is studied. Firstly, a simplified two stage fuzzy curves method is proposed, which is employed to sort all possible inputs by their relevance with outputs, select the important input variables of the system and identify the structure.Secondly, in order to reduce the complexity of the model, the standard fuzzy c-means clustering algorithm and the recursive least squares algorithm are used to identify the premise parameters and conclusion parameters, respectively. Then, the effectiveness of IVS is verified by two well-known issues. Finally, the proposed identification method is applied to a realistic variable load pneumatic system. The simulation experiments indi cate that the IVS method in this paper has a positive influence on the approximation performance of the Takagi-Sugeno(T-S) fuzzy modeling.
文摘This paper presents a fuzzy logic approach to efficiently perform unsupervised character classification for improvement in robustness, correctness and speed of a character recognition system. The characters are first split into eight typographical categories. The classification scheme uses pattern matching to classify the characters in each category into a set of fuzzy prototypes based on a nonlinear weighted similarity function. The fuzzy unsupervised character classification, which is natural in the repre...
基金The National Natural Science Foundation of China(No.60474049,60835001)Specialized Research Fund for Doctoral Program of Higher Education(No.20090092120027)
文摘In order to overcome data-quantization, networked-induced delay, network packet dropouts and wrong sequences in the nonlinear networked control system, a novel nonlinear networked control system model is built by the T-S fuzzy method. Two time-varying quantizers are added in the model. The key analysis steps in the method are to construct an improved interval-delay-dependent Lyapunov functional and to introduce the free-weighting matrix. By making use of the parallel distributed compensation technology and the convexity of the matrix function, the improved criteria of the stabilization and stability are obtained. Simulation experiments show that the parameters of the controllers and quantizers satisfying a certain performance can be obtained by solving a set of LMIs. The application of the nonlinear mass-spring system is provided to show that the proposed method is effective.
文摘Tourism demand forecasting has attracted substantial interest because of the significant economic contributions of the fast-growing tourism industry. Although various quantitative forecasting techniques have been widely studied, highly accurate and understandable forecasting models have not been developed. The present paper proposes a novel tourism demand forecasting method that extracts fuzzy Takagi-Sugeno (T-S) rules from trained SVMs. Unlike previous approaches, this study uses fuzzy T-S models extracted from the outputs of trained SVMs on tourism data. Owing to the symbolic fuzzy rules and the generalization ability of SVMs, the extracted fuzzy T-S rules exhibit high forecasting accuracy and include understandable pre-condition parts for practitioners. Based on the tourism demand forecasting problem in Hong Kong SAR, China as a case study, empirical findings on tourist arrivals from nine overseas origins reveal that the proposed approach performs comparably with SVMs and can achieve better prediction accuracy than other forecasting techniques for most origins. The findings demonstrated that decision makers can easily interpret fuzzy T-S rules extracted from SVMs. Thus, the approach is highly beneficial to tourism market management. This finding demonstrates the excellent scientific and practical values of the proposed approach in tourism demand forecasting.
文摘A novel multiresolution pyramidal edge detector, based on adaptive weighted fuzzy mean(AWFM)filtering and fuzzy linking model, is presented in this paper. The algorithm first constructs a pyramidal structure by repetitive AWFM filtering and subsampling of original image. Then it utilizes multiple heuristic linking criteria between the edge nodes of two adjacent levels and considers the linkage as a fuzzy model, which is trained offline. Through this fuzzy linking model, the boundaries detected at coarse resolution are propagated and refined to the bottom level from the coarse-to fine edge detection. The validation experiment results demonstrate that the proposed approach has superior performance compared with standard fixed resolution detector andprevious multiresolution approach, especially in impulse noise environment.
基金The National Natural Science Foundation of China(No.60764001,60835001,60875035,61004032)the Postdoctoral Research Fund of Southeast Universitythe Natural Science Foundation of Jiangsu Province(No.BK2008294)
文摘This paper deals with the problem of stabilization design for a class of continuous-time Takagi-Sugeno(T-S)fuzzy systems.New stabilization conditions are derived based on a relaxed approach in which both fuzzy Lyapunov functions and staircase membership functions are used.Through the staircase membership functions approximating the continuous membership functions of the given fuzzy model,the information of the membership functions can be brought into the stabilization design of the fuzzy systems,thereby significantly reducing the conservativeness in the existing stabilization conditions of the T-S fuzzy systems.Unlike some previous fuzzy Lyapunov function approaches reported in the literature,the proposed stabilization conditions do not depend on the time-derivative of the membership functions that may be the main source of conservatism when considering fuzzy Lyapunov functions analysis.Moreover,conditions for the solvability of the controller design are written in the form of linear matrix inequalities,but not bilinear matrix inequalities,which are easier to be solved by convex optimization techniques.A simulation example is given to demonstrate the validity of the proposed approach.