Trigonometric Polynomial Neural Network and its Application to Identification and Control

Y. Yamamoto and H. Yoshimura (Japan)


Fourier analysis, neural networks, identification, functionapproximation, nonlinear model


Trigonometric Polynomial Neural Network (TPNN) is proposed. TPNN can be recognized as a new scheme of neural network based on a trigonometric polynomial which is familiar in Fourier Analysis. The proposed network is linear with respect to its coefficients and the well known recursive least squares method of linear parameter estimation can be used as a learning algorithm. Using the TPNN, learning of nonlinear functions and identification of nonlinear discrete time system are examined with some additional comments for the type of nonlinear systems. The efficiency of the proposed method is also certified by applying the identified system for control of nonlinear discrete time system.

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