Some multistability properties of bidirectional associative memory recurrent neural networks with unsaturating piecewise linear transfer functions

Authors:
Lei Zhang;Zhang Yi;Jiali Yu;Pheng Ann Heng
Affiliations:
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong;College of Computer Science, Sichuan University, Chengdu 610065, P.R. China;School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China;Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong and School of Computer Science and Engineering, University of Electronic Science and Te ...
Venue:
Neurocomputing
Year:
2009

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Abstract

Multistability is an important dynamical property in neural networks in order to enable certain applications where monostable networks could be computationally restrictive. This paper studies some multistability properties for a class of bidirectional associative memory recurrent neural networks with unsaturating piecewise linear transfer functions. Based on local inhibition, conditions for globally exponential attractivity are established. These conditions allow coexistence of stable and unstable equilibrium points. By constructing some energy-like functions, complete convergence is studied.