Learning Adaptive Parameters with Restricted Genetic Optimization Method

Authors:
Santiago Garrido;Luis Moreno
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

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Abstract

Machanisms for adapting models, filters, regulators and so on to changing properties of a system are of fundamental importance in many modern identification, estimation and control algorithms. This paper presents a new method based on Genetic Algorithms to improve the results of other classic methods such as the extended least squares method or the Kalman method. This method simulates the gradient mechanism without using derivatives and for this reason, it is roboust in presence of noise.