Passivity Analysis for Neuro Identifier with Different Time-Scales
Many physical systems contains fast and slow phenomenons. In this paper we propose a dynamic neural networks with different time-scales to model the nonlinear system. Passivity-based approach is used to derive stability conditions for neural identifer. Several stability properties, such as passivity, asymptotic stability, input-to-state stability and bounded input bounded output stability, are guaranteed in certain senses. Numerical examples are also given to demonstrate the effectiveness of the theoretical results.
KeywordsNeural Network Asymptotic Stability Complete Convergence Gradient Descent Algorithm Global Exponential Stability
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