Abstract
Mechanisms 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 amd for this reason, it is robust in presence of noise
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© 2001 Springer-Verlag Berlin Heidelberg
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Garrido, S., Moreno, L. (2001). Learning Adaptive Parameters with Restricted Genetic Optimization Method. In: Mira, J., Prieto, A. (eds) Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence. IWANN 2001. Lecture Notes in Computer Science, vol 2084. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45720-8_73
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DOI: https://doi.org/10.1007/3-540-45720-8_73
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