Please use this identifier to cite or link to this item: https://doi.org/10.1109/TNN.2009.2014373
Title: Permitted and forbidden sets in discrete-time linear threshold recurrent neural networks
Authors: Yi, Z.
Zhang, L.
Yu, J.
Tan, K.K. 
Keywords: Complete convergence
Discrete-time recurrent neural networks
Forbidden set
Linear threshold
Multiattractivity
Permitted set
Issue Date: 2009
Citation: Yi, Z., Zhang, L., Yu, J., Tan, K.K. (2009). Permitted and forbidden sets in discrete-time linear threshold recurrent neural networks. IEEE Transactions on Neural Networks 20 (6) : 952-963. ScholarBank@NUS Repository. https://doi.org/10.1109/TNN.2009.2014373
Abstract: The concepts of permitted and forbidden sets enable a new perspective of the memory in neural networks. Such concepts exhibit interesting dynamics in recurrent neural networks. This paper studies the basic theories of permitted and forbidden sets of the linear threshold discrete-time recurrent neural networks. The linear threshold transfer function has been regarded as an adequate transfer function for recurrent neural networks. Networks with this transfer function form a class of hybrid analog and digital networks which are especially useful for perceptual computations. Networks in discrete time can directly provide algorithms for efficient implementation in digital hardware. The main contribution of this paper is to establish foundations of permitted and forbidden sets. Necessary and sufficient conditions for the linear threshold discrete-time recurrent neural networks are obtained for complete convergence, existence of permitted and forbidden sets, as well as conditionally multiattractivity, respectively. Simulation studies explore some possible interesting practical applications. © 2009 IEEE.
Source Title: IEEE Transactions on Neural Networks
URI: http://scholarbank.nus.edu.sg/handle/10635/57045
ISSN: 10459227
DOI: 10.1109/TNN.2009.2014373
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