Please use this identifier to cite or link to this item: https://doi.org/10.1109/TCSI.2005.846664
Title: Qualitative analysis for recurrent neural networks with linear threshold transfer functions
Authors: Tan, K.C. 
Tang, H.
Zhang, W.
Keywords: Equilibria
Global attractivity
Linear threshod (LT) neural network
Multistability
Nonsaturating
Issue Date: May-2005
Source: Tan, K.C., Tang, H., Zhang, W. (2005-05). Qualitative analysis for recurrent neural networks with linear threshold transfer functions. IEEE Transactions on Circuits and Systems I: Regular Papers 52 (5) : 1003-1012. ScholarBank@NUS Repository. https://doi.org/10.1109/TCSI.2005.846664
Abstract: Multistable networks have attracted much interest in recent years, since multistability is of primary importance for some applications of recurrent neural networks where monostability exhibits some restrictions. This paper focuses on the analysis of dynamical property for a class of additive recurrent neural networks with nonsaturating linear threshold transfer functions. A milder condition is derived to guarantee the boundedness and global attractivity of the networks as compared to that presented in [6]. Dynamical properties of the equilibria of two-dimensional networks are analyzed theoretically, and the relationships between the equilibria features and network parameters (synaptic weights and external inputs) are revealed. In addition, the sufficient and necessary conditions for coexistence of multiple equilibria are obtained, which confirmed the observations in [14] with a cortex-inspired silicon circuit. The results obtained in this paper are applicable to both symmetric and nonsymmetric networks. Simulation examples are used to illustrate the theory developed in this paper. © 2005 IEEE.
Source Title: IEEE Transactions on Circuits and Systems I: Regular Papers
URI: http://scholarbank.nus.edu.sg/handle/10635/57165
ISSN: 10577122
DOI: 10.1109/TCSI.2005.846664
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