Passivity analysis for neuro identifier with different time-scales

Authors:
Alejandro Cruz Sandoval;Wen Yu;Xiaoou Li
Affiliations:
Departamento de Control Automático, CINVESTAV-IPN, México D.F., México;Departamento de Control Automático, CINVESTAV-IPN, México D.F., México;Sección de Computación, Departamento de Ingeniería Eléctrica, CINVESTAV-IPN, México D.F., México
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Year:
2006

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Abstract

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.