Please use this identifier to cite or link to this item: https://doi.org/10.1162/089976603321192112
Title: Multistability analysis for recurrent neural networks with unsaturating piecewise linear transfer functions
Authors: Yi, Z. 
Tan, K.K. 
Lee, T.H. 
Issue Date: Mar-2003
Source: Yi, Z., Tan, K.K., Lee, T.H. (2003-03). Multistability analysis for recurrent neural networks with unsaturating piecewise linear transfer functions. Neural Computation 15 (3) : 639-662. ScholarBank@NUS Repository. https://doi.org/10.1162/089976603321192112
Abstract: Multistability is a property necessary in neural networks in order to enable certain applications (e.g., decision making), where monostable networks can be computationally restrictive. This article focuses on the analysis of multistability for a class of recurrent neural networks with unsaturating piecewise linear transfer functions. It deals fully with the three basic properties of a multistable network: boundedness, global attractivity, and complete convergence. This article makes the following contributions: conditions based on local inhibition are derived that guarantee boundedness of some multistable networks, conditions are established for global attractivity, bounds on global attractive sets are obtained, complete convergence conditions for the network are developed using novel energy-like functions, and simulation examples are employed to illustrate the theory thus developed.
Source Title: Neural Computation
URI: http://scholarbank.nus.edu.sg/handle/10635/56737
ISSN: 08997667
DOI: 10.1162/089976603321192112
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

70
checked on Dec 13, 2017

WEB OF SCIENCETM
Citations

67
checked on Dec 13, 2017

Page view(s)

36
checked on Dec 9, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.