Identification and Adaptive Control of Dynamic Nonlinear Systems Using Sigmoid Diagonal Recurrent Neural Network

Tarek Aboueldahab, Mahumod Fakhreldin

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DOI: 10.4236/ica.2011.23021                

Abstract

The goal of this paper is to introduce a new neural network architecture called Sigmoid Diagonal Recurrent Neural Network (SDRNN) to be used in the adaptive control of nonlinear dynamical systems. This is done by adding a sigmoid weight victor in the hidden layer neurons to adapt of the shape of the sigmoid function making their outputs not restricted to the sigmoid function output. Also, we introduce a dynamic back propagation learning algorithm to train the new proposed network parameters. The simulation results showed that the (SDRNN) is more efficient and accurate than the DRNN in both the identification and adaptive control of nonlinear dynamical systems.

Keywords

Sigmoid Diagonal Recurrent Neural Networks, Dynamic Back Propagation, Dynamic Nonlinear Systems, Adaptive Control

Conflicts of Interest

The authors declare no conflicts of interest.

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