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Nonlinear system modeling using self-organizing fuzzy neural networks for industrial applications

APPLIED INTELLIGENCE 2020年第5期50卷 1657-1672页

作者： Zhou, Hongbiao Zhao, Huanyu Zhang, Yu Huaiyin Inst Technol Fac Automat Huaian 223003 Peoples R China

In this paper, a novel self-organizing fuzzy neural network with an adaptive learning algorithm (SOFNN-ALA) for nonlinear system modeling and identification in industrial processes is proposed. To efficiently enhance the generalization capability, the proposed SOFNN-ALA is designed by using both structure identification and parameter estimation simultaneously in the learning process. In the structure identification phase, the rule neuron with the highest neuronal activity will be split into two new rule neurons. Meanwhile, the redundant rule neurons with small singular values will be removed to simplify the network structure. In the parameter estimation phase, an adaptive learning algorithm (ALA), which is designed based on the widely used Levenberg-Marquardt (LM) optimization algorithm, is adopted to optimize the network parameters. The ALA-based learning algorithm can not only speed up the convergence speed but also enhance the modeling performance. Moreover, we carefully analyze the convergence of the proposed SOFNN-ALA to guarantee its successful practical application. Finally, the effectiveness and efficiency of the proposed SOFNN-ALA is validated by several examples. The experimental results demonstrate that the proposed SOFNN-ALA exhibits a better comprehensive performance than some other state-of-the-art SOFNNs for nonlinear system modeling in industrial applications. The source code can be downloaded from https://***/hyitzhb/***.

关键词： Nonlinear system modeling Self-organizing fuzzy neural network Neuronal activity Singular value decomposition adaptive learning algorithm

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Design and analysis of a novel chaotic diagonal recurrent neural network

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COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION 2015年第1-3期26卷 11-23页

作者： Wang, Libiao Meng, Zhuo Sun, Yize Guo, Lei Zhou, Mingxing Donghua Univ Coll Mech Engn Shanghai 201620 Peoples R China Taizhou Univ Coll Mech Engn Taizhou 318000 Peoples R China

A chaotic neural network model with logistic mapping is proposed to improve the performance of the conventional diagonal recurrent neural network. The network shows rich dynamic behaviors that contribute to escaping from a local minimum to reach the global minimum easily. Then, a simple parameter modulated chaos controller is adopted to enhance convergence speed of the network. Furthermore, an adaptive learning algorithm with the robust adaptive dead zone vector is designed to improve the generalization performance of the network, and weights convergence for the network with the adaptive dead zone vectors is proved in the sense of Lyapunov functions. Finally, the numerical simulation is carried out to demonstrate the correctness of the theory. (C) 2015 Elsevier B.V. All rights reserved.

关键词： adaptive learning algorithm Chaos control Chaotic neural network Weight convergence

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adaptive algorithms and networks for optimal feature extraction from Gaussian data

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PATTERN RECOGNITION LETTERS 2010年第11期31卷 1331-1341页

作者： Ghassabeh, Youness Aliyari Moghaddam, Hamid Abrishami Queens Univ Dept Math & Stat Math & Engn Grp Kingston ON K7L 3N6 Canada

In this paper, new adaptive learning algorithms and correspondent networks are presented in order to extract optimal features from a sequence of multidimensional Gaussian data. For this purpose, novel adaptive algorithms for the estimation of the square root of the inverse covariance matrix Sigma(-1/2) are introduced and applied for Gaussian optimal feature extraction. New adaptive algorithms are drawn by optimization of an appropriate cost function that is presented for the first time. Based on the proposed adaptive algorithms, related networks are implemented in order to extract optimal features from a sequence of multidimensional Gaussian data. adaptive nature of the new feature extraction methods makes them appropriate for on-line pattern recognition applications. Experimental results using multidimensional Gaussian data demonstrated the effectiveness of the new adaptive feature extraction methods. (C) 2010 Elsevier B.V. All rights reserved.

关键词： adaptive learning algorithm Feature extraction Multidimensional gaussian data

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Coupled principal component analysis

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IEEE TRANSACTIONS ON NEURAL NETWORKS 2004年第1期15卷 214-222页

作者： Möller, R Könies, A Max Planck Inst Psychol Res Robot Grp D-80799 Munich Germany Max Planck Inst Plasma Phys Stellarator Theory Grp D-17491 Greifswald Germany

A framework for a class of coupled principal component learning rules is presented. In coupled rules, eigenvectors and eigenvalues of a covariance matrix are simultaneously estimated in coupled equations. Coupled rules can mitigate the stability-speed problem affecting noncoupled learning rules, since the convergence speed in all eigendirections of the Jacobian becomes widely independent of the eigenvalues of the covariance matrix. A number of coupled learning rule systems for principal component analysis, two of them new, is derived by applying Newton's method to an information criterion. The relations to other systems of this class, the adaptive learning algorithm (ALA), the robust recursive least squares algorithm (RRLSA), and a rule with explicit renormalization of the weight vector length, are established.

关键词： adaptive learning algorithm minor component analysis (MCA) neural networks Newton's method Oja's rule principal component analysis (PCA) robust recursive least squares learning algorithm speed-stability tradeoff

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A Cognitive Vision Method for Insect Pest Image Segmentation

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IFAC-PapersOnLine 2018年第17期51卷 85-89页

作者： Wang, Zhibin Wang, Kaiyi Liu, Zhongqiang Wang, Xiaofeng Pan, Shouhui Beijing Research Center for Information Technology in Agriculture Beijing 100097 China Faculty of Information Technology Beijing University of Technology Beijing 100124 China

Segmentation of pests from crop leaves is the prerequisite step for pest intelligent diagnosis. To improve the accuracy and stability of segmentation, a cognitive segmentation approach to pest images is present in this paper. The method works in the follow way. First, a pest image is divided into blocks via an image block processing method. Second, an adaptive learning algorithm is used to accurately select the initial cluster centers. Third, preliminary segmentation results are achieved using K-means clustering. Finally, three digital morphological features of an ellipse are adopted to remove leaf veins. Segmentation experiments were performed on crop images of whiteflies. Compared with the conventional methods, the proposed cognitive segmentation method was accurate and robust for segmentation of whitefly images, and will provide a foundation for further identifying these pests. © 2018

关键词： Image segmentation Computer vision Crops Image enhancement K means clustering learning algorithms Pest control adaptive learning algorithm Conventional methods Initial cluster centers Intelligent diagnosis Morphological features Segmentation methods Segmentation results Whitefly

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Sensitivity-Based adaptive learning Rules for Binary Feedforward Neural Networks

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2012年第3期23卷 480-491页

作者： Zhong, Shuiming Zeng, Xiaoqin Wu, Shengli Han, Lixin Hohai Univ Inst Intelligence Sci & Technol Nanjing 210098 Jiangsu Peoples R China Univ Ulster Sch Comp & Math Coleraine BT37 0QB Londonderry North Ireland

This paper proposes a set of adaptive learning rules for binary feedforward neural networks (BFNNs) by means of the sensitivity measure that is established to investigate the effect of a BFNN's weight variation on its output. The rules are based on three basic adaptive learning principles: the benefit principle, the minimal disturbance principle, and the burden-sharing principle. In order to follow the benefit principle and the minimal disturbance principle, a neuron selection rule and a weight adaptation rule are developed. Besides, a learning control rule is developed to follow the burden-sharing principle. The advantage of the rules is that they can effectively guide the BFNN's learning to conduct constructive adaptations and avoid destructive ones. With these rules, a sensitivity-based adaptive learning (SBALR) algorithm for BFNNs is presented. Experimental results on a number of benchmark data demonstrate that the SBALR algorithm has better learning performance than the Madaline rule II and backpropagation algorithms.

关键词： adaptive learning algorithm binary feedforward neural networks learning rule sensitivity

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Differential and geometric properties of Rayleigh quotients with applications

Differential and geometric properties of Rayleigh quotients ...

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IEEE International Symposium on Circuits and Systems

作者： Hasan, Mohammed A. Univ Minnesota Dept Elect & Comp Engn Duluth MN 55812 USA

ISBN: (纸本)9780780393899

In this paper, learning rules are proposed for simultaneous computation of minor eigenvectors of a covariance matrix. To understand the optimality conditions of Rayleigh quotients, many interesting identities and properties related are derived. For example, it is shown that the Hessian matrix is singular at each critical point. of the Rayleigh quotient. Based on these properties, MCA rules are derived by optimizing a weighted inverse Rayleigh quotient so that the optimum weights at equilibrium points are exactly the desired eigenvectors of a covariance matrix instead of an arbitrary orthonormal basis of the minor subspace. Variations of the derived MCA learning rules are obtained by imposing orthogonal and quadratic constraints and change of variables. Some of the proposed algorithms can also perform PCA by merely changing the sign of the step-size.

关键词： minor component analysis principal component analysis adaptive learning algorithm extreme eigenvalues

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A Hybrid CKF-NNPID Controller for MIMO Nonlinear Control System 13

A Hybrid CKF-NNPID Controller for MIMO Nonlinear Control Sys...

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13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)

作者： Sento, Adna Kitjaidure, Yuttana King Mongkuts Inst Technol Ladkrabang Sch Elect Engn Fac Engn Bangkok 10520 Thailand

ISBN: (纸本)9781467397490

This paper presents a detailed study to demonstrate the online tuning dynamic neural network PID controller to improve a joint angle position output performance of 4-joint robotic arm. The proposed controller uses a new updating weight rule model of the neural network architecture using multi-loop calculation of the fusion of the gradient algorithm with the cubature Kalman filter (CKF) which can optimize the internal predicted state of the updated weights to improve the proposed controller performances, called a Hybrid CKF-NNIPD controller. To evaluate the proposed controller performances, the demonstration by the Matlab simulation program is used to implement the proposed controller that connects to the 4-joint robotic arm system. In the experimental result, it shows that the proposed controller is a superior control method comparing with the other prior controllers even though the system is under the loading criteria, the proposed controller still potentially tracks the error and gives the best performances.

关键词： Robotic arm control system neural network PID controller cubature Kalman filters adaptive learning algorithm

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Fuzzy Clustering of Biomedical Datasets Using BSB-Neuro-Fuzzy-Model 1

Fuzzy Clustering of Biomedical Datasets Using BSB-Neuro-Fuzz...

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1st International Workshop on Informatics and Data- Driven Medicine (IDDM)

作者： Perova, Iryna Bodyanskiy, Yevgeniy Sachenko, Anatoliy Karpinski, Mikolaj Rudyk, Pawel Kharkiv Natl Univ Radio Elect UA-61166 Kharkiv Ukraine Kazimierz Pulaski Univ Technol & Humanities Radom Radom Poland Ternopil Natl Econ Univ 3 Peremoha Sq UA-46020 Ternopol Ukraine Univ Bielsko Biala 2 Willowa St PL-43309 Bielsko Biala Poland

A special neural networks that contain autoassociative memory (AM) - BSB- and GBSB-models are investigated at this paper. These models are implemented on hypercube and solve the task of dataset clusterization due to the fact of point attraction properties of hypercube peaks. A BSB-neuro-fuzzy model can be based on BSB-model as well due to the introduction of the special fuzzy membership function. A training algorithm for the BSB- neuro-fuzzy model is proposed. This algorithm enables to enrich the BSB-neuro-fuzzy model by adaptive properties. An experiment based on medical datasets proved a high quality of the proposed model.

关键词： fuzzy clustering hypercube attractor adaptive learning algorithm stable states

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RBF neural network based predictive control of active power filter

RBF neural network based predictive control of active power ...

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IEEE Region 10 Conference on Analog and Digital Techniques in Electrical Engineering

作者： Wang, XH Xiao, JH Changsha Univ Sci & Technol Dept Elect & Informat Engn Changsha Peoples R China

ISBN: (纸本)0780385608

A RBF neural network based predictive control of active power filter is presented in this paper. RBF neural network is employed to predict future harmonic compensating current. In order to make the predictive model much simpler and tighter, an adaptive learning algorithm for RBF network is proposed. Based on the model output, branch-and-bound optimization method is adopted to produce proper value of control vector. This control vector is adequately modulated by means of a space vector PWM modulator which generates proper gating patterns of the inverter switches to maintain tracking of reference current. The RBF neural network based predictive algorithm is used in internal model control scheme to compensate for process disturbances, measurement noise and modeling errors. Experiment on an actual system is implemented. The results show the RBF neural network based predictive control eliminates supply current and voltage harmonics greatly and is more effective than PI control.

关键词： radial basis function networks active filters power harmonic filters learning systems adaptive control predictive control PI control pulse width modulation invertors switches power engineering computing optimisation noise measurement radial basis function RBF neural network active power filter adaptive learning algorithm branch-and-bound optimization method space vector PWM modulator gating patterns inverter switches predictive algorithm internal model control scheme voltage harmonics PI control

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