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Decentralized Semi-Supervised Learning for stochastic Configuration networks Based on the Mean Teacher Method

Journal of Computer and Communications 2024年第4期12卷 247-261页

作者： Kaijing Li Wu Ai School of Mathematics and Statistics Guilin University of Technology Guilin China Guangxi Colleges and Universities Key Laboratory of Applied Statistics Guilin China

The aim of this paper is to broaden the application of stochastic Configuration network (SCN) in the semi-supervised domain by utilizing common unlabeled data in daily life. It can enhance the classification accuracy of decentralized SCN algorithms while effectively protecting user privacy. To this end, we propose a decentralized semi-supervised learning algorithm for SCN, called DMT-SCN, which introduces teacher and student models by combining the idea of consistency regularization to improve the response speed of model iterations. In order to reduce the possible negative impact of unsupervised data on the model, we purposely change the way of adding noise to the unlabeled data. Simulation results show that the algorithm can effectively utilize unlabeled data to improve the classification accuracy of SCN training and is robust under different ground simulation environments.

关键词： stochastic neural network Consistency Regularization Semi-Supervised Learning Decentralized Learning

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Passivity analysis for uncertain stochastic neural networks with discrete interval and distributed time-varying delays

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Journal of Systems Engineering and Electronics 2010年第4期21卷 688-697页

作者： P.Balasubramaniam G.Nagamani Department of Mathematics Gandhigram Rural University

The problem of passivity analysis is investigated for uncertain stochastic neural networks with discrete interval and distributed time-varying *** parameter uncertainties are assumed to be norm bounded and the delay is assumed to be time-varying and belongs to a given interval,which means that the lower and upper bounds of interval time-varying delays are *** constructing proper Lyapunov-Krasovskii functional and employing a combination of the free-weighting matrix method and stochastic analysis technique,new delay-dependent passivity conditions are derived in terms of linear matrix inequalities（LMIs）.Finally,numerical examples are given to show the less conservatism of the proposed conditions.

关键词： linear matrix inequality(LMI) stochastic neural network passivity interval time-varying delay Lyapunov method.

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ASYMPTOTIC STABILITIES OF stochastic FUNCTIONAL DIFFERENTIAL EQUATIONS

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Applied Mathematics and Mechanics(English Edition) 2006年第11期27卷 1577-1584页

作者：沈轶江明辉廖晓昕 Department of Control Science and Engineering Huazhong University of Science and TechnologyWuhan 430074P. R. China

Asymptotic characteristic of solution of the stochastic functional differential equation was discussed and sufficient condition was established by multiple Lyapunov functions for locating the limit set of the solution. Moreover, from them many effective criteria on stochastic asymptotic stability, which enable us to construct the Lyapunov functions much more easily in application, were obtained, The results show that the wellknown classical theorem on stochastic asymptotic stability is a special case of our more general results. In the end, application in stochastic Hopfield neural networks is given to verify our results.

关键词： stochastic functional differential equation stochastic neural network asymptotic stability semi-martingale convergence theorem Ito^ formula

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