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Stability and attractive basins of equilibria in complex-valued recurrent neural networks
Huang Yujiao 1,Wang Zhanshan 2 * #
1.School of Information Science and Engineering, Northeastern University, ShenYang 110004
2.School of Information Science and Engineering, Northeastern University, Shenyang 110004
*Correspondence author
#Submitted by
Subject:
Funding: National Natural Science Foundation of China（No.Grant Nos. 61074073 and 61034005）, Specialized Research Fund for the Doctoral Program of Higher Education（No.Grant No. 200801451096）
Opened online:14 December 2011
Accepted by: none
Citation: Huang Yujiao,Wang Zhanshan.Stability and attractive basins of equilibria in complex-valued recurrent neural networks[OL]. [14 December 2011] http://en.paper.edu.cn/en_releasepaper/content/4454491
 
 
This paper is concerned with multistability of complex-valued recurrent neural networks with a class nondecreasing piecewise linear activation function. By using decomposition of state space, sufficient conditions are established to ensure that n-dimensional complex-valued neural networks with a general class of activation function can have 9^n equilibria, 4^n of them are locally exponentially stable and others are unstable. Moreover, the attractive basins of equilibria are investigated. The results improve and extend the existing stability results in the literatures. One simulation example is given to illustrate the effectiveness of the results.
Keywords:Multistability; complex-valued recurrent neural networks; activation function
 
 
 

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