Identification of Nonlinear Systems Based on a Genetically Trained Fuzzy Neural Network

Authors

• Omar F. Al-Karkhy;, Control & Systems Engineering/University of Technology Baghdad, Iraq
• Intisar A. Al-Said, Control&SystemsEngineering/UniversityofTechnologyBaghdadIraq
• I. Al-Dulaimy, Control & Systems Engineering/University of Technology Baghdad, Iraq

Document Type

Original Study

Abstract

Networks (NNs) and Genetic Algorithms (GAs), where the GA is used to train a Fuzzy Neural Identifier (FNI) to identify ill-defined dynamical systems using the series-parallel identification model.The parameters of the FNI (including the input and output scaling factors, the centers and widths of the membership functions (MFs) for the input variables, and the quantization levels of the output variable, that are subjected to constraints on their values by the expert) are modified by the real-coding GA with hybrid selection method and elitism strategy based on minimizing the Mean Square of Error (MSE) criterion.The simulation results for modeling three different nonlinear plants show the effectiveness of this FNI

How to Cite This Article

Al-Karkhy;, Omar F.; Al-Said, Intisar A.; and Al-Dulaimy, I. (2004) "Identification of Nonlinear Systems Based on a Genetically Trained Fuzzy Neural Network," Iraqi Journal of Computers, Communications, Control and Systems Engineering: Vol. 4: Iss. 1, Article 3.
Available at: https://ijccce.researchcommons.org/journal/vol4/iss1/3