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learning algorithm for pattern classification using cellular neural networks

ELECTRONICS LETTERS 2000年第23期36卷 1941-1943页

作者： Grassi, G Di Sciascio, E Univ Lecce Dipartimento Ingn Innovazione I-73100 Lecce Italy Politecn Bari Dipartimento Elettrotecn & Elettron I-70125 Bari Italy

A new learning algorithm for pattern classification using cellular neural networks is described. The authors show that patterns belonging to the training set as well as patterns outside it can be classified reliably using the proposed algorithm. Comparisons with well established classification techniques clearly highlight the performances of the approach developed herein.

关键词： learning (artificial intelligence) Neural nets training set pattern classification cellular neural nets learning in ai classification techniques Pattern recognition learning algorithm cellular neural networks

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Competitive neural network scheme for learning vector quantisation

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ELECTRONICS LETTERS 1999年第9期35卷 725-726页

作者： Wang, JH Peng, CY Natl Taiwan Ocean Univ Dept Elect Engn Keelung Taiwan

A novel self-development neural network scheme, which employs two resource counters to record node activity, is presented. The proposed network not only harmonises equi-error and equiprobable criteria, but it also avoids the stability-and-plasticity dilemma. Simulation results show that the new scheme displays superior performance (in terms of measured MSE, MAE, and training speed) over other neural network models.

关键词： equi-error criteria neural nets node activity Neural nets competitive neural network scheme vector quantisation unsupervised learning learning vector quantisation mean absolute error MAE learning in ai resource counters MSE equi-probable criteria self-development neural network scheme Interpolation and function approximation (numerical analysis) mean square error methods training speed

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Magnified gradient function in adaptive learning: the MGFPROP algorithm

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ELECTRONICS LETTERS 2001年第1期37卷 42-43页

作者： Ng, SC Cheung, CC Leung, SH Inst Vocat Educ Chai Wan Dept Comp & Math Chai Wan Hong Kong Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

A new algorithm is proposed to solve the 'flat spot' problem in back-propagation neural networks by magnifying the gradient function. Simulation results show that, in terms of the convergence rate and the perc... 详细信息

关键词： backpropagation neural networks convergence Neural nets convergence rate adaptive learning magnified gradient function MGFPROP algorithm learning in ai backpropagation global convergence flat spot problem solution feedforward neural nets

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Outlier-resistant adaptive filtering based on sparse Bayesian learning

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ELECTRONICS LETTERS 2014年第9期50卷 663-+页

作者： Zhu, Wei Tang, Jun Wan, Shuang Zhu, Jie-Li Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

In adaptive processing applications, the design of the adaptive filter requires estimation of the unknown interference-plus-noise covariance matrix from secondary training data. The presence of outliers in the training data can severely degrade the performance of adaptive processing. By exploiting the sparse prior of the outliers, a Bayesian framework to develop a computationally efficient outlier-resistant adaptive filter based on sparse Bayesian learning (SBL) is proposed. The expectation-maximisation (EM) algorithm is used therein to obtain a maximum a posteriori (MAP) estimate of the interference-plus-noise covariance matrix. Numerical simulations demonstrate the superiority of the proposed method over existing methods.

关键词： interference (signal) secondary training data Electromagnetic compatibility and interference MAP estimation Interpolation and function approximation (numerical analysis) EM algorithm Signal processing theory covariance matrices filtering theory SBL unknown interference-plus-noise covariance matrix estimation outlier-resistant adaptive filtering adaptive processing applications Bayes methods Linear algebra (numerical analysis) Other topics in statistics learning (artificial intelligence) Filtering methods in signal processing adaptive filters learning in ai expectation-maximisation algorithm maximum a posteriori estimation sparse Bayesian learning

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Optimal learning for patterns classification in RBF networks

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ELECTRONICS LETTERS 2002年第20期38卷 1188-1190页

作者： Hoang, TA Nguyen, DT Univ Tasmania Sch Engn Hobart Tas 7001 Australia

The proposed modifying of the structure of the radial basis function (RBF) network by introducing the weight matrix to the input layer (in contrast to the direct connection of the input to the hidden layer of a conventional RBF) so that the training space in the RBF network is adaptively separated by the resultant decision boundaries and class regions is reported. The training of this weight matrix is carried out as for a single-layer perceptron together with the clustering process. This way the network is capable of dealing with complicated problems, which have a high degree of interference in the training data, and achieves a higher classification rate over the current classifiers using RBF.

关键词： radial basis function networks learning (artificial intelligence) input layer clustering process Neural nets radial basis function network pattern classification class regions learning in ai training space Pattern recognition RBF networks classification rate improvement single-layer perceptron decision boundaries optimal learning weight matrix training

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Combining genetic optimisation with hybrid learning algorithm for radial basis function neural networks

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ELECTRONICS LETTERS 2003年第22期39卷 1600-1601页

作者： Guo, L Huang, DS Zhao, WB Chinese Acad Sci Inst Intelligent Machines Hefei 230031 Peoples R China

A two-step learning scheme for radial basis function neural networks (RBFNN) is proposed. A genetic algorithm initially optimises the parameters of the RBFNN and a hybrid learning algorithm adjusts these parameters fu... 详细信息

关键词： radial basis function networks learning (artificial intelligence) generalisation performance RBFNN two-step learning scheme hybrid learning algorithm radial basis function neural networks Neural nets genetic optimisation Optimisation techniques RBF neural networks genetic algorithms learning in ai genetic algorithm

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Reactive parking control of nonholonomic vehicles via a fuzzy learning automaton

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IEE PROCEEDINGS-CONTROL THEORY AND APPLICATIONS 2001年第2期148卷 169-179页

作者： Rigatos, GG Tzafestas, SG Evangelidis, GJ Natl Tech Univ Athens Dept Elect & Comp Engn Intelligent Robot & Automat Lab GR-15773 Athens Greece

The paper presents an intelligent reactive controller, which is capable of performing automated parallel parking in a confined space slightly larger than the dimensions of the vehicle. This controller combines the features of fuzzy logic control and sliding-mode control and is modelled as a fuzzy learning, automaton. Starting with a brief exposition of the principles of reactive learning;and learning automata, the concept of the reduced complexity sliding-mode fuzzy logic controller (RC-SMFLC), employed, is introduced. Then the solution of the parking problem via the, RC-SMFLC, along with the convergence analysis, is presented. Representative simulation results are provided that demonstrate the parking steps and the effectiveness of the parking controller.

关键词： Automata theory convergence analysis learning in ai computational complexity sliding-mode control reactive parking control fuzzy control reactive learning Adaptive system theory convergence confined space reduced complexity sliding-mode fuzzy logic controller Computational complexity intelligent reactive controller nonholonomic vehicles automated parallel parking Neurocontrol variable structure systems Multivariable control systems learning automata fuzzy logic control fuzzy learning automaton Fuzzy control intelligent control RC-SMFLC Road-traffic system control automobiles

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Iterative learning control: quantifying the effect of output noise

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IET CONTROL THEORY AND APPLICATIONS 2011年第2期5卷 379-388页

作者： Owens, D. H. Liu, S. Univ Sheffield Automat Control & Syst Engn Dept Sheffield S1 3JD S Yorkshire England

Fixed parameter iterative learning control (ILC) for linear-time invariant, single-input single-output systems subject to output noise is analysed with the intent of predicting the expectation of the underlying 'noise-free' mean square error (Euclidean norm) of the time series on each iteration. Explicit formulae are obtained in terms of the 'lifted' matrix models of the plant. Computational experiments are used to confirm the correctness of the proposed properties. Finally, frequency domain formulae are derived to provide insight into links between plant characteristics, noise spectra and other ILC parameters, and illustrated by application to the inverse-model-based ILC algorithm.

关键词： learning in ai time series Interpolation and function approximation (numerical analysis) frequency-domain analysis noise spectra output noise linear-time invariant Linear algebra (numerical analysis) matrix models single-input single-output systems iterative learning control mean square error methods learning (artificial intelligence) mean square error frequency domain formulae matrix algebra iterative methods

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New SV selection strategy and local-global regularisation method for improving online SVM learning

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ELECTRONICS LETTERS 2018年第12期54卷 735-736页

作者： Tang, Tinglong Chen, Shengyong Luo, Jake Zhejiang Univ Technol Collaborat Innovat Ctr Yangtze River Delta Reg Green Pharmaceut Hangzhou 310032 Zhejiang Peoples R China Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310032 Zhejiang Peoples R China Univ Wisconsin Milwaukee Dept Hlth Informat & Adm Milwaukee WI 53211 USA China Three Gorges Univ Coll Comp & Informat Technol Yichang 443002 Peoples R China Tianjin Univ Technol Sch Comp Sci & Engn Tianjin 300384 Peoples R China

During the online learning process of support vector machines (SVMs), when a newly added sample is violating the Karush-Kuhn-Tucker) conditions, the new sample should be a new SV and transfer the old samples between the SVs and the non-SVs. Normally, the performance of an SVM model is decided by the SVs, and the model should be updated by the newly added SVs;therefore, the selection of high-quality candidate SVs will lead to a better learning accuracy, whereas low-quality candidate SVs may result in low learning efficiency and unnecessary updating. A new strategy is proposed to select the candidate SVs. SVs are selected according to two new criteria: the importance and the informativeness criteria. Furthermore, a mixed local-global regularisation method is applied during the online learning process to improve the penalty coefficients. Experiment results show that the proposed algorithm can achieve a better performance with a faster speed and a higher accuracy when compared with traditional methods.

关键词： learning in ai learning (artificial intelligence) online SVM learning mixed local–global regularisation method penalty coefficients SVM model support vector machines SV selection strategy Karush–Kuhn–Tucker conditions

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Memristor-based neuron circuit and method for applying learning algorithm in SPICE

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ELECTRONICS LETTERS 2014年第7期50卷 492-493页

作者： Yakopcic, C. Hasan, R. Taha, T. M. McLean, M. Palmer, D. Univ Dayton Dayton OH 45409 USA Ctr Except Comp Catonsville MD 21228 USA Annapolis Microsyst Annapolis MD 21401 USA

The learning of nonlinearly separable functions in cascaded memristor crossbar circuits is described and the feasibility of using them to develop low-power neuromorphic processors is demonstrated. This is the first study evaluating the training of memristor crossbars through SPICE simulations. It is important to capture the alternate current paths and wire resistance inherent in these circuits. The simulations show that neural network learning algorithms are able to train in the presence of alternate current paths and wire resistances. The fact that the approach reduces the area by three times and power by two orders of magnitude compared with the existing approaches that use virtual ground opamps to eliminate alternate current paths is demonstrated.

关键词： Electronic engineering computing low-power neuromorphic processors wire resistance SPICE learning in ai cascaded memristor crossbar circuits memristor-based neuron circuit Resistors Neural nets (circuit implementations) memristors neural network learning algorithms Computer-aided circuit analysis and design Neural computing techniques learning (artificial intelligence) alternate current paths virtual ground op-amps neural nets

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