Multistability for Delayed Neural Networks via Sequential Contracting

IEEE Transactions on Neural Networks and Learning Systems
Chang-Yuan ChengJui-Pin Tseng

Abstract

In this paper, we explore a variety of new multistability scenarios in the general delayed neural network system. Geometric structure embedded in equations is exploited and incorporated into the analysis to elucidate the underlying dynamics. Criteria derived from different geometric configurations lead to disparate numbers of equilibria. A new approach named sequential contracting is applied to conclude the global convergence to multiple equilibrium points of the system. The formulation accommodates both smooth sigmoidal and piecewise-linear activation functions. Several numerical examples illustrate the present analytic theory.

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Citations

May 3, 2016·Neural Networks : the Official Journal of the International Neural Network Society·Peng LiuJun Wang
Jul 6, 2017·IEEE Transactions on Neural Networks and Learning Systems·Peng LiuJun Wang
Apr 26, 2021·Neural Networks : the Official Journal of the International Neural Network Society·Fanghai ZhangZhigang Zeng
Aug 18, 2021·Neural Networks : the Official Journal of the International Neural Network Society·Xiaobing NieJinde Cao

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