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

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

Mitsuo Yoshida, Takehiro Mori
Copyright: © 2009 |Pages: 19
ISBN13: 9781605662145|ISBN10: 1605662143|ISBN13 Softcover: 9781616925628|EISBN13: 9781605662152
DOI: 10.4018/978-1-60566-214-5.ch005
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MLA

Yoshida, Mitsuo, and Takehiro Mori. "Global Stability Analysis for Complex-Valued Recurrent Neural Networks and Its Application to Convex Optimization Problems." Complex-Valued Neural Networks: Utilizing High-Dimensional Parameters, edited by Tohru Nitta, IGI Global, 2009, pp. 104-122. https://doi.org/10.4018/978-1-60566-214-5.ch005

APA

Yoshida, M. & Mori, T. (2009). Global Stability Analysis for Complex-Valued Recurrent Neural Networks and Its Application to Convex Optimization Problems. In T. Nitta (Ed.), Complex-Valued Neural Networks: Utilizing High-Dimensional Parameters (pp. 104-122). IGI Global. https://doi.org/10.4018/978-1-60566-214-5.ch005

Chicago

Yoshida, Mitsuo, and Takehiro Mori. "Global Stability Analysis for Complex-Valued Recurrent Neural Networks and Its Application to Convex Optimization Problems." In Complex-Valued Neural Networks: Utilizing High-Dimensional Parameters, edited by Tohru Nitta, 104-122. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-214-5.ch005

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Abstract

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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