A new method for adaptive model-based control of dynamic industrial plants using neural networks, fuzzy logic and fractal theory

Authors:
Patricia Melin;Oscar Castillo
Affiliations:
Department of Computer Science, Tijuana Institute of Technology, P.O. Box 4207, Chula Vista CA;Department of Computer Science, Tijuana Institute of Technology, P.O. Box 4207, Chula Vista CA
Venue:
Systems Analysis Modelling Simulation
Year:
2003

Citing 7
Cited 0

Structure identification of fuzzy model

Fuzzy Sets and Systems
Stable adaptive systems

Stable adaptive systems
A bioreactor benchmark for adaptive network-based process control

Neural networks for control
Numerical analysis and graphic visualization with MATLAB

Numerical analysis and graphic visualization with MATLAB
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence

Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Automated mathematical modelling and simulation for bacteria growth control in the food industry using artificial intelligence and fractal theory

Systems Analysis Modelling Simulation
Control Engineering Solutions: A Practical Approach

Control Engineering Solutions: A Practical Approach

Quantified Score

Hi-index	0.00

Visualization

Abstract

We describe in this paper a new method for adaptive model-based control of non-linear dynamic plants using Neural Networks, Fuzzy Logic and Fractal Theory. The new neuro-fuzzy-fractal method combines Soft Computing (SC) techniques with the concept of the fractal dimension for the domain of Non-Linear Dynamic Plant Control. The new method for adaptive model-based control has been implemented as a computer program to show that our neuro-fuzzy-fractal approach is a good alternative for controlling non-linear dynamic plants. We illustrate in this paper our new methodology with the case of controlling biochemical reactors in the food industry. For this case, we use mathematical models for the simulation of bacteria growth for several types of food. The goal of constructing these models is to capture the dynamics of bacteria population in food, so as to have a way of controlling this dynamics for industrial purposes.