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ZJUS-A
Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering)  2007, Vol. 8 Issue (12): 1912-1920    DOI: 10.1631/jzus.2007.A1912
Electrical & Electronic Engineering     
Robust exponential stability analysis of a larger class of discrete-time recurrent neural networks
ZHANG Jian-hai, ZHANG Sen-lin, LIU Mei-qin
Department of System Science & Engineering, School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
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Abstract  The robust exponential stability of a larger class of discrete-time recurrent neural networks (RNNs) is explored in this paper. A novel neural network model, named standard neural network model (SNNM), is introduced to provide a general framework for stability analysis of RNNs. Most of the existing RNNs can be transformed into SNNMs to be analyzed in a unified way. Applying Lyapunov stability theory method and S-Procedure technique, two useful criteria of robust exponential stability for the discrete-time SNNMs are derived. The conditions presented are formulated as linear matrix inequalities (LMIs) to be easily solved using existing efficient convex optimization techniques. An example is presented to demonstrate the transformation procedure and the effectiveness of the results.

Key words： Standard neural network model (SNNM)      Robust exponential stability      Recurrent neural networks (RNNs)      Discrete-time      Time-delay system      Linear matrix inequality (LMI)     
Received: 02 March 2007     
CLC:  TP183  
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ZHANG Jian-hai
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ZHANG Jian-hai, ZHANG Sen-lin, LIU Mei-qin. Robust exponential stability analysis of a larger class of discrete-time recurrent neural networks. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2007, 8(12): 1912-1920.

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http://www.zjujournals.com/xueshu/zjus-a/10.1631/jzus.2007.A1912     OR     http://www.zjujournals.com/xueshu/zjus-a/Y2007/V8/I12/1912

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