Nonlinear Parametric Model Identification with Genetic Algorithms. Application to a Thermal Process

Authors:
Xavier Blasco Ferragud;Juan Manuel Herrero Durá;Miguel Andres Martínez Iranzo;Juan S. Senent Español
Affiliations:
-;-;-;-
Venue:
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Year:
2001

Citing 1
Cited 1

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Abstract

Abstract. One of the first steps taken in any technological area is building a mathematical model. In fact, in the case of process control, modelling is a crucial aspect that influences quality control. Building a nonlinear model is a traditional problem. This paper illustrates how to built an accurate nonlinear model combining first principle modelling and a parametric identification, using Genetic Algorithms. All the experiments presented in this paper are designed for a thermal process.