Please use this identifier to cite or link to this item:
|Title:||Permitted and forbidden sets in discrete-time linear threshold recurrent neural networks|
Discrete-time recurrent neural networks
|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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Nov 14, 2018
WEB OF SCIENCETM
checked on Nov 6, 2018
checked on Oct 27, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.