Search. Read. Cite.

Easy to search. Easy to read. Easy to cite with credible sources.

Research Article
A Radial Basic Function with Multiple Input and Multiple Output Neural Network to Control a Non-Linear Plant of Unknown Dynamics

R. El-Kouatly and G.A. Salman

Information Technology Journal, 2008, 7(3), 430-439.

Abstract

In this study, the application of the Radial Basis Function (RBF) with Multiple Input and Multiple Output (MIMO) Neural networks to control two types of non linear model plants of unknown dynamics. For the first step a model of a control was developed using the variable liquid-level which can be use in a chemical plant, or power station, where the liquid-level is change within fixed real time. In this control system Radial Basis Function (RBF) neural networks was used to control the liquid-level of the plant. The second step introduced the changes of the liquid-level in real time, also Radial Basis Function with MIMO neural networks has been used to control the level liquid. The study shows that the proposed control system produces accurate results for the two types of models. However, we notice that the training, using back propagation, for the second model take a more considerable time than training the first model.

ASCI-ID: 28-38

Cited References Fulltext

Related Articles


Fuzzy Process Neural Network based on Orthogonal Basis Function

Information Technology Journal, 2011, 10(10), 1999-2003.

Neuro Computing for Micro Structural Analysis

Information Technology Journal, 2006, 5(2), 332-335.

Cited By


Adaptive Neuro-PID Controller Design with Application to Nonlinear Water Level in NEKA Power Plant

Journal of Applied Sciences, 2009, 9(19), 3513. DOI: 10.3923/jas.2009.3513.3521

An Approach to Identify Behavior Parameter in Image-based Visual Servo Control

Information Technology Journal, 2012, 11(2), 217. DOI: 10.3923/itj.2012.217.224