Abstract
This paper presents a new combined neural-genetic method for identifying processes comprising static nonlinearities and linear dynamics, and for adapting the model to linear and nonlinear parameter changes. The approach is based on multilayer and recurrent neural networks. It is shown that the powerful properties of genetic algorithms may be used to advantage in a learning neural network. We solve a number of problems and compare the results and the method with those previously reported by Narendra & Parthasarathy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Narendra, K.S. & Parthasarathy, K. Identification and control of dynamical systems using neural networks, IEEE Trans. on Neural Networks, 1, 1, 4–27 (1990).
Goldberg, D.E. Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Publishing Co. (1989).
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1995 Springer-Verlag/Wien
About this paper
Cite this paper
Abu-Alola, A.H., Gough, N.E. (1995). Identification and Adaptive Control of Nonlinear Processes Using Combined Neural Networks and Genetic Algorithms. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7535-4_103
Download citation
DOI: https://doi.org/10.1007/978-3-7091-7535-4_103
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-82692-8
Online ISBN: 978-3-7091-7535-4
eBook Packages: Springer Book Archive