In this paper,an adaptive neural-network(NN)output feedback optimal control problem is studied for a class of strict-feedback nonlinear systems with unknown internal dynamics,input saturation and state constraints.Neu...In this paper,an adaptive neural-network(NN)output feedback optimal control problem is studied for a class of strict-feedback nonlinear systems with unknown internal dynamics,input saturation and state constraints.Neural networks are used to approximate unknown internal dynamics and an adaptive NN state observer is developed to estimate immeasurable states.Under the framework of the backstepping design,by employing the actor-critic architecture and constructing the tan-type Barrier Lyapunov function(BLF),the virtual and actual optimal controllers are developed.In order to accomplish optimal control effectively,a simplified reinforcement learning(RL)algorithm is designed by deriving the updating laws from the negative gradient of a simple positive function,instead of employing existing optimal control methods.In addition,to ensure that all the signals in the closed-loop system are bounded and the output can follow the reference signal within a bounded error,all state variables are confined within their compact sets all times.Finally,a simulation example is given to illustrate the effectiveness of the proposed control strategy.展开更多
In this paper, we prove the existence and the global exponential stability of the unique weighted pseudo almost-periodic solution of shunting inhibitory cellular neural networks with mixed time-varying delays comprisi...In this paper, we prove the existence and the global exponential stability of the unique weighted pseudo almost-periodic solution of shunting inhibitory cellular neural networks with mixed time-varying delays comprising different discrete and distributed time delays. Some sufficient conditions are given for the existence and the global exponential stability of the weighted pseudo almost-periodic solution by employing fixed point theorem and differential inequality techniques. The results of this paper complement the previously known ones. Finally, an illustrative example is given to demonstrate the effectiveness of our results.展开更多
The cone-shaped kernel distributions of vibration acceleration signals, whichwere acquired from the cylinder head in eight different states of a valve train, were calculatedand displayed in grey images. Probabilistic ...The cone-shaped kernel distributions of vibration acceleration signals, whichwere acquired from the cylinder head in eight different states of a valve train, were calculatedand displayed in grey images. Probabilistic Neural Networks ( PAW) was used to classify the imagesdirectly after the images were normalized. By this way, the problem of fault diagnosis for a valvetrain was transferred to the classification of time-frequency images. As there is no need to extractfeatures from time-frequency images before classification, the fault diagnosis process is highlysimplified. The experimental results show that the vibration signals can be classified accurately bythe proposed methods.展开更多
The problem of the global exponential stability of a class of Hopfield neural networks is considered. Based on nonnegative matrix theory, a sufficient condition for the existence, uniqueness and global exponential sta...The problem of the global exponential stability of a class of Hopfield neural networks is considered. Based on nonnegative matrix theory, a sufficient condition for the existence, uniqueness and global exponential stability of the equilibrium point is presented. And the upper bound for the degree of exponential stability is given. Moreover, a simulation is given to show the effectiveness of the result.展开更多
In fault diagnosis of rotating machinery, Hil- bert-Huang transform (HHT) is often used to extract the fault characteristic signal and analyze decomposition results in time-frequency domain. However, end effect occu...In fault diagnosis of rotating machinery, Hil- bert-Huang transform (HHT) is often used to extract the fault characteristic signal and analyze decomposition results in time-frequency domain. However, end effect occurs in HHT, which leads to a series of problems such as modal aliasing and false IMF (Intrinsic Mode Func- tion). To counter such problems in HHT, a new method is put forward to process signal by combining the general- ized regression neural network (GRNN) with the bound- ary local characteristic-scale continuation (BLCC). Firstly, the improved EMD (Empirical Mode Decompo- sition) method is used to inhibit the end effect problem that appeared in conventional EMD. Secondly, the gen- erated IMF components are used in HHT. Simulation and measurement experiment for the cases of time domain, frequency domain and related parameters of Hilbert- Huang spectrum show that the method described here can restrain the end effect compared with the results obtained through mirror continuation, as the absolute percentage of the maximum mean of the beginning end point offset and the terminal point offset are reduced from 30.113% and 27.603% to 0.510% and 6.039% respectively, thus reducing the modal aliasing, and eliminating the false IMF components of HHT. The proposed method caneffectively inhibit end effect, reduce modal aliasing and false IMF components, and show the real structure of signal components accuratelX.展开更多
Sheet metal forming is widely applied to automobile, aviation, space flight, ship, instrument, and appliance industries.In this paper, based on analyzing the shortcoming of general finite element analysis (FEA), the c...Sheet metal forming is widely applied to automobile, aviation, space flight, ship, instrument, and appliance industries.In this paper, based on analyzing the shortcoming of general finite element analysis (FEA), the conception of parametric finite element analysis (PFEA) is presented. The parametric finite element analysis, artificial neural networks(ANN) and genetic algorithm (GA) are combined to research thoroughly on the problems of process parametersoptimization of sheet metal forming. The author programs the optimization scheme and applies it in a research ofoptimization problem of inside square hole flanging technological parameters. The optimization result coincides wellwith the result of experiment. The research shows that the optimization scheme offers a good new way in die designand sheet metal forming field.展开更多
Handwritten signature recognition is presented based on an angle feature vector by using the artificial neural network (ANN) in this research. Each signature image will be represented by an angle vector. The feature...Handwritten signature recognition is presented based on an angle feature vector by using the artificial neural network (ANN) in this research. Each signature image will be represented by an angle vector. The feature vector will constitute the input to the ANN. The collection of signature images is divided into two sets. One set will be used for training the ANN in a supervised fashion. The other set which is never seen by the ANN will be used for testing. After training, the ANN will be tested by recognizing the signatures. When a signature is classified correctly, it is considered correct recognition, otherwise it is a failure. The achieved recognition rate of this system is 94%.展开更多
Recently, deep neural networks, which include convolutional neural networks(CNNs), have been widely applied to acoustic scene classification(ASC). Motivated by the fact that some simplified CNNs have shown improve...Recently, deep neural networks, which include convolutional neural networks(CNNs), have been widely applied to acoustic scene classification(ASC). Motivated by the fact that some simplified CNNs have shown improvements over deep CNNs, such as Visual Geometry Group Net(VGG-Net), we have figured out how to simplify the VGG-Net style architecture to a shallow CNN with improved performance. Max pooling and batch normalization are also applied for better accuracy. With a series of controlled tests on detection and classification of acoustic scenes and events(DCASE) 2016 data sets, our shallow CNN achieves 6.7% improvement, and reduces time complexity to 5%, compared with the VGG-Net style CNN.展开更多
An on-line method was developed to improve diagnostic accuracy and speed for analyzing run- ning motors on site. On-line pre-measured data was used as the basis for constructing the membership functions used in a fuzz...An on-line method was developed to improve diagnostic accuracy and speed for analyzing run- ning motors on site. On-line pre-measured data was used as the basis for constructing the membership functions used in a fuzzy neural network (FNN) as well as for network training to reduce the effects of various static factors, such as unbalanced input power and asymmetrical motor alignment, to increase accuracy. The preprocessed data and fuzzy logic were used to find the nonlinear mapping relationships between the data and the conclusions, The FNN was then constructed to carry motor fault diagnostics, which gives fast accurate diagnostics. The on-line fast motor fault diagnostics clearly indicate the fault type, location, and severity in running motors. This approach can also be extended to other applications.展开更多
The distribution of the geometric distances of connected neurons is a practical factor underlying neural networks in the brain. It can affect the brain's dynamic properties at the ground level. Karbowski derived a po...The distribution of the geometric distances of connected neurons is a practical factor underlying neural networks in the brain. It can affect the brain's dynamic properties at the ground level. Karbowski derived a power-law decay distribution that has not yet been verified by experiment. In this work, we check its validity using simulations with a phenomenological model. Based on the in vitro two- dimensional development of neural networks in culture vessels by Ito, we match the synapse number saturation time to obtain suitable parameters for the development process, then determine the distri-bution of distances between connected neurons under such conditions. Our simulations obtain a clear exponential distribution instead of a power-law one, which indicates that Karbowski's conclusion is invalid, at least for the case of in vitro neural network development in two-dimensional culture vessels.展开更多
The objective of this paper was to predict the residual strength of post impacted carbon/epoxy composite laminates using an online acoustic emission (AE) monitoring and artificial neural networks (ANN). The lamina...The objective of this paper was to predict the residual strength of post impacted carbon/epoxy composite laminates using an online acoustic emission (AE) monitoring and artificial neural networks (ANN). The laminates were made from eight-layered carbon (in woven mat form) with epoxy as the binding medium by hand lay-up technique and cured at a pressure of 100 kg/cm2 under room temperature using a 30 ton capacity compression molding machine for 24 h. 21 tensile specimens (ASTM D3039 standard) were cut from the cross ply laminates. 16 specimens were subjected to impact load from three different heights using a Fractovis Plus drop impact tester. Both impacted and non-impacted specimens were subjected to uniaxial tension under the acoustic emission monitoring using a 100 kN FIE servo hydraulic universal testing machine. The dominant AE parameters such as counts, energy, duration, rise time and amplitude are recorded during monitoring. Cumulative counts corresponding to the amplitude ranges obtained during the tensile testing are used to train the network. This network can be used to predict the failure load of a similar specimen subjected to uniaxial tension under acoustic emission monitoring for certain percentage of the average failure load.展开更多
Based on the ensemble mean outputs of the ensemble forecasts from the ECMWF (European Centre for Medium-Range Weather Forecasts), JMA (Japan Meteorological Agency), NCEP (National Centers for Environmental Predic...Based on the ensemble mean outputs of the ensemble forecasts from the ECMWF (European Centre for Medium-Range Weather Forecasts), JMA (Japan Meteorological Agency), NCEP (National Centers for Environmental Prediction), and UKMO (United Kingdom Met Office) in THORPEX (The Observing System Research and Predictability Experiment) Interactive Grand Global Ensemble (TIGGE) datasets, for the Northern Hemisphere (10~ 87.5~N, 0~ 360~) from i June 2007 to 31 August 2007, this study carried out multimodel ensemble forecasts of surface temperature and 500-hPa geopotential height, temperature and winds up to 168 h by using the bias-removed ensemble mean (BREM), the multiple linear regression based superensemble (LRSUP), and the neural network based superensemble (NNSUP) techniques for the forecast period from 8 to 31 August 2007. A running training period is used for BREM and LRSUP ensemble forecast techniques. It is found that BREM and LRSUP, at each grid point, have different optimal lengths of the training period. In general, the optimal training period for BREM is less than 30 days in most areas, while for LRSUP it is about 45 days.展开更多
The shear strength parameters of soil (cohesion and angle of internal friction) are quite essential in solving many civil engineering problems. In order to determine these parameters, laboratory tests are used. The ...The shear strength parameters of soil (cohesion and angle of internal friction) are quite essential in solving many civil engineering problems. In order to determine these parameters, laboratory tests are used. The main objective of this work is to evaluate the potential of Artificial Neural Network (ANN) and Regression Tree (CART) techniques for the indirect estimation of these parameters. Four different models, considering different combinations of 6 inputs, such as gravel %, sand %, silt %, clay %, dry density, and plasticity index, were investigated to evaluate the degree of their effects on the prediction of shear parameters. A performance evaluation was carried out using Correlation Coefficient and Root Mean Squared Error measures. It was observed that for the prediction of friction angle, the performance of both the techniques is about the same. However, for the prediction of cohesion, the ANN technique performs better than the CART technique. It was further observed that the model considering all of the 6 input soil parameters is the most appropriate model for the prediction of shear parameters. Also, connection weight and bias analyses of the best neural network (i.e., 6/2/2) were attempted using Connec- tion Weight, Garson, and proposed Weight-bias approaches to characterize the influence of input variables on shear strength parameters. It was observed that the Connection Weight Approach provides the best overall methodology for accurately quantifying variable importance, and should be favored over the other approaches examined in this study.展开更多
Biodiesel is an alternative fuel to replace fossil- based diesel fuel. It has fuel properties similar to diesel which are generally determined experimentally. The experimental determination of various properties of bi...Biodiesel is an alternative fuel to replace fossil- based diesel fuel. It has fuel properties similar to diesel which are generally determined experimentally. The experimental determination of various properties of biodiesel is costly, time consuming and a tedious process. To solve these problems, artificial neural network (ANN) has been considered as a vital tool for estimating the fuel properties of biodiesel, especially from its fatty acid (FA) composition. In this study, four ANNs have been designed and trained to predict the cetane number (CN), flash point (FP), kinematic viscosity (KV) and density of biodiesel using ANN with logsig and purelin transfer functions in the hidden layer of all the networks. The five most prevalent FAs from 55 feedstocks found in the literature utilized as the input parameters for the model are palmitic, stearic, oleic, linoleic and linolenie acids except for density network with a sixth parameter (temperature). Other FAs that are present in the biodiesels have been considered based on the number of carbon atom chains and the level of saturation. From this study, the prediction accuracy and the average absolute deviation of the networks are CN (96.69%; 1.637%), KV (95.80%; 1.638%), FP (99.07%; 0.997%) and density (99.40%; 0.101%). These values are reasonably better compared to previous studies on empirical correlations and ANN predictions of these fuel properties found in literature. Hence, the present study demonstrates the ability of ANN model to predict fuel properties of biodiesel with high accuracy.展开更多
The real dynamic thrust measurement system usually tends to be nonlinear due to the complex characteristics of the rig, pipes connection, etc. For a real dynamic measuring system, the nonlinearity must be eliminated b...The real dynamic thrust measurement system usually tends to be nonlinear due to the complex characteristics of the rig, pipes connection, etc. For a real dynamic measuring system, the nonlinearity must be eliminated by some adequate methods. In this paper, a nonlinear model of dynamic thrust measurement system is established by using radial basis function neural network (RBF-NN), where a novel multi-step force generator is designed to stimulate the nonlinearity of the system, and a practical compensation method for the measurement system using left inverse model is proposed. Left inverse model can be considered as a perfect dynamic compensation of the dynamic thrust measurement system, and in practice, it can be approximated by RBF-NN based on least mean square (LMS) algorithms. Different weights are set for producing the multi-step force, which is the ideal input signal of the nonlinear dynamic thrust measurement system. The validity of the compensation method depends on the engine's performance and the tolerance error 0.5%, which is commonly demanded in engineering. Results from simulations and experiments show that the practical compensation using left inverse model based on RBF-NN in dynamic thrust measuring system can yield high tracking accuracy than the conventional methods.展开更多
基金This work was supported by National Natural Science Foundation of China(61822307,61773188).
文摘In this paper,an adaptive neural-network(NN)output feedback optimal control problem is studied for a class of strict-feedback nonlinear systems with unknown internal dynamics,input saturation and state constraints.Neural networks are used to approximate unknown internal dynamics and an adaptive NN state observer is developed to estimate immeasurable states.Under the framework of the backstepping design,by employing the actor-critic architecture and constructing the tan-type Barrier Lyapunov function(BLF),the virtual and actual optimal controllers are developed.In order to accomplish optimal control effectively,a simplified reinforcement learning(RL)algorithm is designed by deriving the updating laws from the negative gradient of a simple positive function,instead of employing existing optimal control methods.In addition,to ensure that all the signals in the closed-loop system are bounded and the output can follow the reference signal within a bounded error,all state variables are confined within their compact sets all times.Finally,a simulation example is given to illustrate the effectiveness of the proposed control strategy.
文摘In this paper, we prove the existence and the global exponential stability of the unique weighted pseudo almost-periodic solution of shunting inhibitory cellular neural networks with mixed time-varying delays comprising different discrete and distributed time delays. Some sufficient conditions are given for the existence and the global exponential stability of the weighted pseudo almost-periodic solution by employing fixed point theorem and differential inequality techniques. The results of this paper complement the previously known ones. Finally, an illustrative example is given to demonstrate the effectiveness of our results.
文摘The cone-shaped kernel distributions of vibration acceleration signals, whichwere acquired from the cylinder head in eight different states of a valve train, were calculatedand displayed in grey images. Probabilistic Neural Networks ( PAW) was used to classify the imagesdirectly after the images were normalized. By this way, the problem of fault diagnosis for a valvetrain was transferred to the classification of time-frequency images. As there is no need to extractfeatures from time-frequency images before classification, the fault diagnosis process is highlysimplified. The experimental results show that the vibration signals can be classified accurately bythe proposed methods.
基金supported by Sichuan Province Foundation for Applied Basic Research and Leaders of Science and Technology under Grant No.05JY029-068-2
文摘The problem of the global exponential stability of a class of Hopfield neural networks is considered. Based on nonnegative matrix theory, a sufficient condition for the existence, uniqueness and global exponential stability of the equilibrium point is presented. And the upper bound for the degree of exponential stability is given. Moreover, a simulation is given to show the effectiveness of the result.
基金Supported by National Natural Science Foundation of China(Grant No.51375467)Quality Inspection of Public Welfare Industry Research Projects,China(Grant No.201410009)
文摘In fault diagnosis of rotating machinery, Hil- bert-Huang transform (HHT) is often used to extract the fault characteristic signal and analyze decomposition results in time-frequency domain. However, end effect occurs in HHT, which leads to a series of problems such as modal aliasing and false IMF (Intrinsic Mode Func- tion). To counter such problems in HHT, a new method is put forward to process signal by combining the general- ized regression neural network (GRNN) with the bound- ary local characteristic-scale continuation (BLCC). Firstly, the improved EMD (Empirical Mode Decompo- sition) method is used to inhibit the end effect problem that appeared in conventional EMD. Secondly, the gen- erated IMF components are used in HHT. Simulation and measurement experiment for the cases of time domain, frequency domain and related parameters of Hilbert- Huang spectrum show that the method described here can restrain the end effect compared with the results obtained through mirror continuation, as the absolute percentage of the maximum mean of the beginning end point offset and the terminal point offset are reduced from 30.113% and 27.603% to 0.510% and 6.039% respectively, thus reducing the modal aliasing, and eliminating the false IMF components of HHT. The proposed method caneffectively inhibit end effect, reduce modal aliasing and false IMF components, and show the real structure of signal components accuratelX.
基金This work was supported by the Natural Science Foundation of the Jiangxi Province of China under grant No.12 and the Committee of Science and Technology of Jiangxi Province, China.
文摘Sheet metal forming is widely applied to automobile, aviation, space flight, ship, instrument, and appliance industries.In this paper, based on analyzing the shortcoming of general finite element analysis (FEA), the conception of parametric finite element analysis (PFEA) is presented. The parametric finite element analysis, artificial neural networks(ANN) and genetic algorithm (GA) are combined to research thoroughly on the problems of process parametersoptimization of sheet metal forming. The author programs the optimization scheme and applies it in a research ofoptimization problem of inside square hole flanging technological parameters. The optimization result coincides wellwith the result of experiment. The research shows that the optimization scheme offers a good new way in die designand sheet metal forming field.
基金supported by the University of Misurata,Libya and the College of Industrial Technology,Libya
文摘Handwritten signature recognition is presented based on an angle feature vector by using the artificial neural network (ANN) in this research. Each signature image will be represented by an angle vector. The feature vector will constitute the input to the ANN. The collection of signature images is divided into two sets. One set will be used for training the ANN in a supervised fashion. The other set which is never seen by the ANN will be used for testing. After training, the ANN will be tested by recognizing the signatures. When a signature is classified correctly, it is considered correct recognition, otherwise it is a failure. The achieved recognition rate of this system is 94%.
基金Supported by the National Natural Science Foundation of China(61102127,61231015)National High Technology Research and Development Program of China(863 Program,2015AA016306)+3 种基金National Key Research and Development Program(2016YFB0502204)the Innovation Fund of Shanghai Aerospace Science and Technology(SAST,2015014)the Key Technology R&D Program of Hubei Provence(2014BAA153)SKLSE-2015-A-06
文摘Recently, deep neural networks, which include convolutional neural networks(CNNs), have been widely applied to acoustic scene classification(ASC). Motivated by the fact that some simplified CNNs have shown improvements over deep CNNs, such as Visual Geometry Group Net(VGG-Net), we have figured out how to simplify the VGG-Net style architecture to a shallow CNN with improved performance. Max pooling and batch normalization are also applied for better accuracy. With a series of controlled tests on detection and classification of acoustic scenes and events(DCASE) 2016 data sets, our shallow CNN achieves 6.7% improvement, and reduces time complexity to 5%, compared with the VGG-Net style CNN.
基金Supported by the Macao Science and Technology Development Foundation(No.007/2006/A1)
文摘An on-line method was developed to improve diagnostic accuracy and speed for analyzing run- ning motors on site. On-line pre-measured data was used as the basis for constructing the membership functions used in a fuzzy neural network (FNN) as well as for network training to reduce the effects of various static factors, such as unbalanced input power and asymmetrical motor alignment, to increase accuracy. The preprocessed data and fuzzy logic were used to find the nonlinear mapping relationships between the data and the conclusions, The FNN was then constructed to carry motor fault diagnostics, which gives fast accurate diagnostics. The on-line fast motor fault diagnostics clearly indicate the fault type, location, and severity in running motors. This approach can also be extended to other applications.
文摘The distribution of the geometric distances of connected neurons is a practical factor underlying neural networks in the brain. It can affect the brain's dynamic properties at the ground level. Karbowski derived a power-law decay distribution that has not yet been verified by experiment. In this work, we check its validity using simulations with a phenomenological model. Based on the in vitro two- dimensional development of neural networks in culture vessels by Ito, we match the synapse number saturation time to obtain suitable parameters for the development process, then determine the distri-bution of distances between connected neurons under such conditions. Our simulations obtain a clear exponential distribution instead of a power-law one, which indicates that Karbowski's conclusion is invalid, at least for the case of in vitro neural network development in two-dimensional culture vessels.
文摘The objective of this paper was to predict the residual strength of post impacted carbon/epoxy composite laminates using an online acoustic emission (AE) monitoring and artificial neural networks (ANN). The laminates were made from eight-layered carbon (in woven mat form) with epoxy as the binding medium by hand lay-up technique and cured at a pressure of 100 kg/cm2 under room temperature using a 30 ton capacity compression molding machine for 24 h. 21 tensile specimens (ASTM D3039 standard) were cut from the cross ply laminates. 16 specimens were subjected to impact load from three different heights using a Fractovis Plus drop impact tester. Both impacted and non-impacted specimens were subjected to uniaxial tension under the acoustic emission monitoring using a 100 kN FIE servo hydraulic universal testing machine. The dominant AE parameters such as counts, energy, duration, rise time and amplitude are recorded during monitoring. Cumulative counts corresponding to the amplitude ranges obtained during the tensile testing are used to train the network. This network can be used to predict the failure load of a similar specimen subjected to uniaxial tension under acoustic emission monitoring for certain percentage of the average failure load.
基金Supported by the China Meteorological Administration Special Public Welfare Research Fund (GYHY(QX)2007-6-1)National Key Basic Research and Development (973) Program of China (2012CB955204)
文摘Based on the ensemble mean outputs of the ensemble forecasts from the ECMWF (European Centre for Medium-Range Weather Forecasts), JMA (Japan Meteorological Agency), NCEP (National Centers for Environmental Prediction), and UKMO (United Kingdom Met Office) in THORPEX (The Observing System Research and Predictability Experiment) Interactive Grand Global Ensemble (TIGGE) datasets, for the Northern Hemisphere (10~ 87.5~N, 0~ 360~) from i June 2007 to 31 August 2007, this study carried out multimodel ensemble forecasts of surface temperature and 500-hPa geopotential height, temperature and winds up to 168 h by using the bias-removed ensemble mean (BREM), the multiple linear regression based superensemble (LRSUP), and the neural network based superensemble (NNSUP) techniques for the forecast period from 8 to 31 August 2007. A running training period is used for BREM and LRSUP ensemble forecast techniques. It is found that BREM and LRSUP, at each grid point, have different optimal lengths of the training period. In general, the optimal training period for BREM is less than 30 days in most areas, while for LRSUP it is about 45 days.
文摘The shear strength parameters of soil (cohesion and angle of internal friction) are quite essential in solving many civil engineering problems. In order to determine these parameters, laboratory tests are used. The main objective of this work is to evaluate the potential of Artificial Neural Network (ANN) and Regression Tree (CART) techniques for the indirect estimation of these parameters. Four different models, considering different combinations of 6 inputs, such as gravel %, sand %, silt %, clay %, dry density, and plasticity index, were investigated to evaluate the degree of their effects on the prediction of shear parameters. A performance evaluation was carried out using Correlation Coefficient and Root Mean Squared Error measures. It was observed that for the prediction of friction angle, the performance of both the techniques is about the same. However, for the prediction of cohesion, the ANN technique performs better than the CART technique. It was further observed that the model considering all of the 6 input soil parameters is the most appropriate model for the prediction of shear parameters. Also, connection weight and bias analyses of the best neural network (i.e., 6/2/2) were attempted using Connec- tion Weight, Garson, and proposed Weight-bias approaches to characterize the influence of input variables on shear strength parameters. It was observed that the Connection Weight Approach provides the best overall methodology for accurately quantifying variable importance, and should be favored over the other approaches examined in this study.
文摘Biodiesel is an alternative fuel to replace fossil- based diesel fuel. It has fuel properties similar to diesel which are generally determined experimentally. The experimental determination of various properties of biodiesel is costly, time consuming and a tedious process. To solve these problems, artificial neural network (ANN) has been considered as a vital tool for estimating the fuel properties of biodiesel, especially from its fatty acid (FA) composition. In this study, four ANNs have been designed and trained to predict the cetane number (CN), flash point (FP), kinematic viscosity (KV) and density of biodiesel using ANN with logsig and purelin transfer functions in the hidden layer of all the networks. The five most prevalent FAs from 55 feedstocks found in the literature utilized as the input parameters for the model are palmitic, stearic, oleic, linoleic and linolenie acids except for density network with a sixth parameter (temperature). Other FAs that are present in the biodiesels have been considered based on the number of carbon atom chains and the level of saturation. From this study, the prediction accuracy and the average absolute deviation of the networks are CN (96.69%; 1.637%), KV (95.80%; 1.638%), FP (99.07%; 0.997%) and density (99.40%; 0.101%). These values are reasonably better compared to previous studies on empirical correlations and ANN predictions of these fuel properties found in literature. Hence, the present study demonstrates the ability of ANN model to predict fuel properties of biodiesel with high accuracy.
文摘The real dynamic thrust measurement system usually tends to be nonlinear due to the complex characteristics of the rig, pipes connection, etc. For a real dynamic measuring system, the nonlinearity must be eliminated by some adequate methods. In this paper, a nonlinear model of dynamic thrust measurement system is established by using radial basis function neural network (RBF-NN), where a novel multi-step force generator is designed to stimulate the nonlinearity of the system, and a practical compensation method for the measurement system using left inverse model is proposed. Left inverse model can be considered as a perfect dynamic compensation of the dynamic thrust measurement system, and in practice, it can be approximated by RBF-NN based on least mean square (LMS) algorithms. Different weights are set for producing the multi-step force, which is the ideal input signal of the nonlinear dynamic thrust measurement system. The validity of the compensation method depends on the engine's performance and the tolerance error 0.5%, which is commonly demanded in engineering. Results from simulations and experiments show that the practical compensation using left inverse model based on RBF-NN in dynamic thrust measuring system can yield high tracking accuracy than the conventional methods.