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Stability Prediction of Nonlinear System Using Multilayer Feed-forward Artificial Neural Network

D.R. Marpaka, Mohammad Bodruzzaman, Sadra Rahimi Kari, S. Al Sharaeah

Open publisher page 2 citations

Abstract

Stability analysis of linear systems can be studied by root-locus technique, Nyquist criterion and Routh Hurwitz's stability testing. Stability study of nonlinear systems can be done by describing function method, phase-plane analysis, numerical integration, and Lyapunov's second method. In this paper, neural network approach to predict the stability of nonlinear systems is presented. This approach is illustrated with an example.

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What this paper is about

Stability analysis of linear systems can be studied by root-locus technique, Nyquist criterion and Routh Hurwitz's stability testing. Stability study of nonlinear systems can be done by describing function method, phase-plane analysis, numerical integration, and Lyapunov's second method. In this paper, neural network approach to predict the stability of nonlinear systems is presented. This approach is illustrated with an example.

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OpenAlex reports 2 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Stability analysis of linear systems can be studied by root-locus technique, Nyquist criterion and Routh Hurwitz's stability testing. Stability study of nonlinear systems can be done by describing function method, phase-plane analysis, numerical integration, and Lyapunov's second method. In this paper, neural network approach to predict the stability of nonlinear systems is presented. This approach is illustrated with an example.

Key concepts: Circle criterion, Nyquist stability criterion, Root locus, Artificial neural network, Nonlinear system, Control theory (sociology), Stability (learning theory), Describing function

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