On-line nonlinear systems identification of coupled tanks via fractional differential neural networks

Authors:
Arefeh Boroomand;Mohammad Bagher Menhaj
Affiliations:
Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran;Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

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Abstract

Fractional differential neural network (FDNN) is the extended neural network using fractional-order operators. On-line nonlinear system identification using FDNN is studied in this paper. Here all states of the non-linear system are assumed to be available in the system output. Through Lyapunov-like analysis, the fractional neural network parameters are adjusted so it will be proven that the identification error becomes bounded and tends to zero. To illustrate the applicability of the FDNN as a nonlinear identifier, two coupled tanks are considered as a case study. The results of simulation are very promising.