Tracking maneuvering target based on neural fuzzy network with incremental neural leaning

The scheme for tracking maneuvering target based on neural fuzzy network with incremental neural learning is proposed. When tracked target maneuver occurs, the scheme can detect maneuver immediately and estimate the maneuver value accurately , then the tracking filter can be compensated correctly and duly by the estimated maneuver value. When environment changes, neural fuzzy network with incremental neural learning （INL-SONFIN） can find its optimal structure and parameters automatically to adopt to changed environment. So, it always produce estimated output very close to the true maneuver value that leads to good tracking performance and avoids misstracking. Simulation results show that the performance is superior to the traditional schemes and the scheme can fit changed dynamic environment to track maneuvering target accurately and duly.

Dynamic Routing Combined to Forecast the Behavior of Traffic in the City of Sao Paulo Using Neuro Fuzzy Network

The challenge of keeping and getting new customers drives the development of new practices to meet the consumption needs of increasingly tends to micro-segmentation of product and consumer market. The new consumption habits of brazilians brought new prospects for market. The objective of this paper is to develop of a dynamic vehicle routing system supported by the behavior of urban traffic in the city ofSao Paulo using Neuro Fuzzy Network. The methodology of this paper consists in the capture of relevant events that interfere with the flow of traffic of the city of Sao Paulo and implementation of a Fuzzy Neural Network trained with these events in order to foresee the traffic behavior. The system offers three labels of hierarchical routing, thus is possible to consider not only the basic factors of routing, but too external factors that directly influence on the flow of traffic and the disruption which may be avoided in large cities, through alternative routes （dynamic vehicle routing）. Predicting the behavior of traffic represents the strategic level routing, dynamic vehicle routing is the tactical level, and routing algorithms to the operational level. This paper will not be discussed the operational level.

Study on fasART neuro-fuzzy networks for distinguishing the difficulty degree of top coal caving in steep seam

Distinguishing the difficulty degree of top coal caving was a precondition of the popularization and application of the roadway sub-level caving in steep seam. Because of complexity and uncertainty of the coal seam, the expression of influence factors was diffi-culty with exact data. According to the fuzzy and uncertainty of influence factors, triangular fuzzy membership functions were adopted to carry out the factors ambiguity, of which the factors not only have the consistency of semantic meaning, but also dissolve sufficiently expert knowledge. Based on the properties and structures of fasART fuzzy neural net-works of fuzzy logic system and practical needs, a simplified fasART model was put for-ward, stability and reliability of the network were improved, the deficiency of learning sam-ples and uncertainty of the factors were better treated. The method is of effective and practical value was identified by experiments.

APPLICATION OF MULTI-SENSOR DATA FUSION BASED ON FUZZY NEURAL NETWORK IN ROTA TING MECHANICAL FAILURE DIAGNOSIS

At present, multi-se nsor fusion is widely used in object recognition and classification, since this technique can efficiently improve the accuracy and the ability of fault toleranc e. This paper describes a multi-sensor fusion system, which is model-based and used for rotating mechanical failure diagnosis. In the data fusion process, the fuzzy neural network is selected and used for the data fusion at report level. By comparing the experimental results of fault diagnoses based on fusion data wi th that on original separate data,it is shown that the former is more accurate than the latter.

INDUCTION MOTOR SPEED CONTROL SYSTEM BASED ON FUZZY NEURAL NETWORK

A fuzzy neural network controller with the teaching controller guidance and parameter regulations for vector-controlled induction motor is proposed. The design procedures of the fuzzy neural controller and the teaching controller are described. The parameters of the membership function are regulated by an on-line learning algorithm. The speed responses of the system under the condition, where the target functions are chosen as I qs and ω, are analyzed. The system responses with the variant of parameter moment of inertial J, viscous coefficients B and torque constant K tare also analyzed. Simulation results show that the control scheme and the controller have the advantages of rapid speed response and good robustness.

AN INTELLIGENT TOOL CONDITION MONITORING SYSTEM USING FUZZY NEURAL NETWORKS

Reliable on line cutting tool conditioning monitoring is an essential feature of automatic machine tool and flexible manufacturing system (FMS) and computer integrated manufacturing system (CIMS). Recently artificial neural networks (ANNs) are used for this purpose in conjunction with suitable sensory systems. The present work in Norwegian University of Science and Technology (NTNU) uses back propagation neural networks (BP) and fuzzy neural networks (FNN) to process the cutting tool state data measured with force and acoustic emission (AE) sensors, and implements a valuable on line tool condition monitoring system using the ANNs. Different ANN structures are designed and investigated to estimate the tool wear state based on the fusion of acoustic emission and force signals. Finally, four case studies are introduced for the sensing and ANN processing of the tool wear states and the failures of the tool with practical experiment examples. The results indicate that a tool wear identification system can be achieved using the sensors integration with ANNs, and that ANNs provide a very effective method of implementing sensor integration for on line monitoring of tool wear states and abnormalities.

Whole process prediction model of silicon steel strip on transverse thickness difference based on Takagi-Sugeno fuzzy network

The hot rolling and cold rolling control models of silicon steel strip were examined.Shape control of silicon steel strip of hot rolling was a theoretical analysis model,and the shape control of cold rolling was a data-based prediction model.The mathematical model of the hot-rolled silicon steel section,including the crown genetic model,inter-stand crown recovery model,and hot-rolled strip section prediction model,is used to control the shape of hot-rolled strip.The cold rolling shape control is mainly based on Takagi-Sugeno fuzzy network,which is used to simulate and predict the transverse thickness difference of cold-rolled silicon steel strip.Finally,a predictive model for the transverse thickness difference of silicon steel strips is developed to provide a new quality control method of transverse thickness of combined hot and cold rolling to improve the strip profile quality and increase economic efficiency.The qualified rate of the non-oriented silicon steel strip is finally obtained by applying this model,and it has been steadily upgraded to meet the needs of product quality and flexible production.

On the Optimization Problem of Spanning Tree in Fuzzy Network

The optimization problem of spanning tree is an important one with wide application in the network theory. This paper extends the optimization problem of spanning tree in nonWzzy network into fony network and, therefore, founds three models concerning the optimization problem of spanning tree in fuzzy network: a-Ma model, MFC model and MFEC model, for which correSPonding algorithms are provided and computed colnplerity analyzed.

Adaptive price adjustment method for used mobile phone based on dual deep fuzzy networks

Aiming at solving the problem that it is challenging to choose the appropriate price adjustment strategy according to the market fluctuations,an adaptive price adjustment method based on dual deep fuzzy networks(DDFN)is designed.First,a price adjustment model based on DDFN is established.Through interactively learning the recycling market environment,the description of the mapping relationship between the market environment information and the price adjustment action is realized.Second,based on a greedy strategy to calculate the optimal price adjustment action,it is possible to make small adjustments based on the preliminary estimated value of the waste mobile phone,and complete the judgment of the mobile phone recycling price.Third,based on the market feedback,the gradient descent algorithm is used to update parameters of the model to improve the performance.The proposed adaptive price adjustment method based on DDFN is applied to the actual transaction process,and the results show that the proposed method can ensure the accuracy and reliability of the adjustment results of the mobile phone recycling price.

Jaya Honey Badger optimization- based deep neuro-fuzzy network structure for detection of (SARS- CoV) Covid-19 disease by using respiratory sound signals

Purpose-The Covid 19 prediction process is more indispensable to handle the spread and deathocurred rate because of Covid-19.However early and precise prediction of Covid-19 is more difcult because of different sizes and resolutions of input image Thus these challenges and problems experienced by traditional Covid-19 detection methods are considered as major motivation to develop JHBO-based DNFN.Design/methodology/approach-The major contribution of this research is to desigm an ffectualCovid-19 detection model using devised JHBObased DNFN,Here,the audio signal is considered as input for detecting Covid-19.The Gaussian filter is applied to input signal for removing the noises and then feature extraction is performed.The substantial features,like spectral rlloff.spectral bandwidth,Mel-frequency,cepstral coefficients (MFCC),spectral flatness,zero crossing rate,spectral centroid,mean square energy and spectral contract are extracted for further processing.Finally,DNFN is applied for detecting Covid 19 and the deep leaning model is trained by designed JHBO algorithm.Accordingly.the developed JHBO method is newly desigmed by inoorporating Honey Badger optimization Algorithm(HBA)and.Jaya algorithm.Findings-The performance of proposed hybrid optimization-based deep learming algorithm is estimated by meansof twoperformance metrics,namely testing accuracy,sensitivity and speificity of 09176,09218 and 09219.Research limitations/implications-The JHBO-based DNFN approach is developed for Covid-19 detection.The developed approach can be extended by including other hybrid optimization algorithms as well as other features can be extracted for further improving the detection performance.Practical implications-The proposed Covid-19 detection method is useful in various applications,like medical and so on,Originality/value-Developed JHBO-enabled DNFN for Covid-19 detection:An effective Covid-19 detection technique is introduced based on hybrid optimization-driven deep learning model The DNFN is used for detecting Covid-19,which classifies the feature vector as Covid-19 or non-Covid 19.Moreover,the DNFN is trained by devised JHB0 approach,which is introduced by combining HBA and Jaya algorithm.

Improving Land Resource Evaluation Using Fuzzy Neural Network Ensembles

Land evaluation factors often contain continuous-, discrete- and nominal-valued attributes. In traditional land evaluation, these different attributes are usually graded into categorical indexes by land resource experts, and the evaluation results rely heavily on experts＇ experiences. In order to overcome the shortcoming, we presented a fuzzy neural network ensemble method that did not require grading the evaluation factors into categorical indexes and could evaluate land resources by using the three kinds of attribute values directly. A fuzzy back propagation neural network （BPNN）, a fuzzy radial basis function neural network （RBFNN）, a fuzzy BPNN ensemble, and a fuzzy RBFNN ensemble were used to evaluate the land resources in Guangdong Province. The evaluation results by using the fuzzy BPNN ensemble and the fuzzy RBFNN ensemble were much better than those by using the single fuzzy BPNN and the single fuzzy RBFNN, and the error rate of the single fuzzy RBFNN or fuzzy RBFNN ensemble was lower than that of the single fuzzy BPNN or fuzzy BPNN ensemble, respectively. By using the fuzzy neural network ensembles, the validity of land resource evaluation was improved and reliance on land evaluators＇ experiences was considerably reduced.

Fuzzy Neural Network Model of 4-CBA Concentration for Industrial Purified Terephthalic Acid Oxidation Process

A fuzzy neural network (FNN) model is developed to predict the 4-CBA concentration of the oxidation unit in purified terephthalic acid process. Several technologies are used to deal with the process data before modeling.First,a set of preliminary input variables is selected according to prior knowledge and experience. Secondly,a method based on the maximum correlation coefficient is proposed to detect the dead time between the process variables and response variables. Finally, the fuzzy curve method is used to reduce the unimportant input variables.The simulation results based on industrial data show that the relative error range of the FNN model is narrower than that of the American Oil Company (AMOCO) model. Furthermore, the FNN model can predict the trend of the 4-CBA concentration more accurately.

Modeling of grain size in isothermal compression of Ti-6Al-4V alloy using fuzzy neural network

Isothermal compression of Ti-6Al-4V alloy was conducted in the deformation temperature range of 1093-1303 K, the strain rates of 0.001, 0.01, 0.1, 1.0, and 10.0 s-1, and the height reductions of 20%-60% with an interval of 10%. After compression, the effect of the processing parameters including deformation temperature, strain rate, and height reduction on the flow stress and the microstructure was investigated. The grain size of primary a phase was measured using an OLYMPUS PMG3 microscope with the quantitative metallography SISC IAS V8.0 image analysis software. A model of grain size in isothermal compression of Ti-6A1-4V alloy was developed using fuzzy neural net- work （FNN） with back-propagation （BP） learning algorithm. The maximum difference and the average difference between the predicted and the experimental grain sizes of primary a phase are 13.31% and 7.62% for the sampled data, and 16.48% and 6.97% for the non-sampled data, respectively. It can be concluded that the present model with high prediction precision can be used to predict the grain size in isothermal compression of Ti-6Al-4V alloy.

Modeling of Energy Consumption and Effluent Quality Using Density Peaks-based Adaptive Fuzzy Neural Network

Modeling of energy consumption(EC) and effluent quality(EQ) are very essential problems that need to be solved for the multiobjective optimal control in the wastewater treatment process(WWTP). To address this issue, a density peaks-based adaptive fuzzy neural network(DP-AFNN) is proposed in this study. To obtain suitable fuzzy rules, a DP-based clustering method is applied to fit the cluster centers to process nonlinearity.The parameters of the extracted fuzzy rules are fine-tuned based on the improved Levenberg-Marquardt algorithm during the training process. Furthermore, the analysis of convergence is performed to guarantee the successful application of the DPAFNN. Finally, the proposed DP-AFNN is utilized to develop the models of EC and EQ in the WWTP. The experimental results show that the proposed DP-AFNN can achieve fast convergence speed and high prediction accuracy in comparison with some existing methods.

A Short-Term Climate Prediction Model Based on a Modular Fuzzy Neural Network

In terms of the modular fuzzy neural network (MFNN) combining fuzzy c-mean (FCM) cluster and single-layer neural network, a short-term climate prediction model is developed. It is found from modeling results that the MFNN model for short-term climate prediction has advantages of simple structure, no hidden layer and stable network parameters because of the assembling of sound functions of the self-adaptive learning, association and fuzzy information processing of fuzzy mathematics and neural network methods. The case computational results of Guangxi flood season (JJA) rainfall show that the mean absolute error (MAE) and mean relative error (MRE) of the prediction during 1998-2002 are 68.8 mm and 9.78%, and in comparison with the regression method, under the conditions of the same predictors and period they are 97.8 mm and 12.28% respectively. Furthermore, it is also found from the stability analysis of the modular model that the change of the prediction results of independent samples with training times in the stably convergent interval of the model is less than 1.3 mm. The obvious oscillation phenomenon of prediction results with training times, such as in the common back-propagation neural network (BPNN) model, does not occur, indicating a better practical application potential of the MFNN model.

Fuzzy neural network control of underwater vehicles based on desired state programming

Due to the nonlinearity and uncertainty, the precise control of underwater vehicles in some intelligent operations hasn’t been solved very well yet. A novel method of control based on desired state programming was presented, which used the technique of fuzzy neural network. The structure of fuzzy neural network was constructed according to the moving characters and the back propagation algorithm was deduced. Simulation experiments were conducted on general detection remotely operated vehicle. The results show that there is a great improvement in response and precision over traditional control, and good robustness to the model’s uncertainty and external disturbance, which has theoretical and practical value.

Research on Prediction of Red Tide Based on Fuzzy Neural Network

In this paper, a four-layer fuzzy neural network using the Back Propagation （BP） Algorithm and the fuzzy logic was built to study the nonlinear relationships between different physical -chemical factors and the denseness of red tide algae, and to anticipate the denseness of the red tide algae. For the first time, the fuzzy neural network technology was applied to research the prediction of red tide. Compared with BP network and RBF network, the outcome of this method is better.

Design of robust fuzzy controller for ship course-tracking based on RBF network and backstepping approach

This study presents an adaptive fuzzy neural network (FNN) control system for the ship steering autopilot. For the Norrbin ship steering mathematical model with the nonlinear and uncertain dynamic characteristics, an adaptive FNN control system is designed to achieve high-precision track control via the backstepping approach. In the adaptive FNN control system, a FNN backstepping controller is a principal controller which includes a FNN estimator used to estimate the uncertainties, and a robust controller is designed to compensate the shortcoming of the FNN backstepping controller. All adaptive learning algorithms in the adaptive FNN control system are derived from the sense of Lyapunov stability analysis, so that system-tracking stability can be guaranteed in the closed-loop system. The effectiveness of the proposed adaptive FNN control system is verified by simulation results.

Self-organizing fuzzy clustering neural network and application to electronic countermeasures effectiveness evaluation

A self-organizing fuzzy clustering neural network by combining the self-organizing Kohonen clustering network with the fuzzy theory is proposed. This network model is designed for the effectiveness evaluation of electronic countermeasures, which not only exerts the advantages of the fuzzy theory, but also has a good ability in machine learning and data analysis. The subjective value of sample versus class is computed by the fuzzy computing theory, and the classified results obtained by self-organizing learning of Kohonen neural network are represented on output layer. Meanwhile, the fuzzy competition learning algorithm keeps the similar information between samples and overcomes the disadvantages of neural network which has fewer samples. The simulation result indicates that the proposed algorithm is feasible and effective.

Adaptive control of parallel manipulators via fuzzy-neural network algorithm

This paper considers adaptive control of parallel manipulators combined with fuzzy-neural network algorithms （FNNA）. With this algorithm, the robustness is guaranteed by the adaptive control law and the parametric uncertainties are eliminated. FNNA is used to handle model uncertainties and external disturbances. In the proposed control scheme, we consider modifying the weight of fuzzy rules and present these rules to a MIMO system of parallel manipulators with more than three degrees-of-freedom （DoF）. The algorithm has the advantage of not requiring the inverse of the Jacobian matrix especially for the low DoF parallel manipulators. The validity of the control scheme is shown through numerical simulations of a 6-RPS parallel manipulator with three DoF.