Design of New Hybrid Neural Structure for Modeling and Controlling Nonlinear Systems

  • Ahmed Sabah Al-Araji Control & Systems Eng. Dept. University of Technology
  • Shaymaa Jafe'er Al-Zangana Control & Systems Eng. Dept. University of Technology
Keywords: NARMA-L2Model, MLP neural Network, Modified Elman Neural Network, Back Propagation Algorithm, Nonlinear CSTR System.

Abstract

This paper proposes a new structure of the hybrid neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Weight parameters of the hybrid neural structure with its serial-parallel configuration are adapted by using the Back propagation learning algorithm. The ability of the proposed hybrid neural structure for nonlinear system has achieved a fast learning with minimum number of epoch, minimum number of neurons in the hybrid network, high accuracy in the output without oscillation response as well as useful model for a one step ahead prediction controller for the nonlinear CSTR system that is used in the MATLAB simulation.

 

Downloads

Download data is not yet available.
Published
2019-01-31
How to Cite
Al-Araji, A. and Al-Zangana, S. (2019) “Design of New Hybrid Neural Structure for Modeling and Controlling Nonlinear Systems”, Journal of Engineering, 25(2), pp. 116-135. doi: 10.31026/j.eng.2019.02.08.