Stability Analysis of a General Class of Continuous-Time Recurrent Neural Networks

2009 ◽  
pp. 340-346
Author(s):  
Chaojin Fu ◽  
Zhongsheng Wang
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Recurrent Neural Networks ◽  
Continuous Time ◽  
General Class

Global Stability Analysis for Complex-Valued Recurrent Neural Networks and Its Application to Convex Optimization Problems

2009 ◽  
pp. 104-122 ◽  
Author(s):  
Mitsuo Yoshida ◽  
Takehiro Mori
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Global Stability ◽  
Discrete Time ◽  
Recurrent Neural Networks ◽  
Continuous Time ◽  
Stability Conditions ◽  
Global Stability Analysis ◽  
Solution Methods ◽  
Complex Valued

Global stability analysis for complex-valued artificial recurrent neural networks seems to be one of yet-unchallenged topics in information science. This chapter presents global stability conditions for discrete-time and continuous- time complex-valued recurrent neural networks, which are regarded as nonlinear dynamical systems. Global asymptotic stability conditions for these networks are derived by way of suitable choices of activation functions. According to these stability conditions, there are classes of discrete-time and continuous-time complex-valued recurrent neural networks whose equilibrium point is globally asymptotically stable. Furthermore, the conditions are shown to be successfully applicable to solving convex programming problems, for which real field solution methods are generally tedious.

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