Abstract
Due to the adaptive characteristics of the learning process, the application of neural networks to nonlinear system identification and control has been developed in a natural way. To cope with the indirect adaptive control problem, two basic approaches have been addressed in the literature. In the first approach, some design problems are learned off-line, measuring the input-output signals and observing the plant behavior in some key situations. The control can then be implemented based on the knowledge acquired: this approach is known as an off-line training/on-line control scheme. In the second approach, an adaptive learning is implemented and the control input is determined on-line as the output of a neural network, which is called on-line learning/on-line control strategy.
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© 2002 Springer Science+Business Media New York
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Sundararajan, N., Saratchandran, P., Li, Y. (2002). Indirect Adaptive Control Using Fully Tuned RBFN. In: Fully Tuned Radial Basis Function Neural Networks for Flight Control. The Springer International Series on Asian Studies in Computer and Information Science, vol 12. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-5286-1_4
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DOI: https://doi.org/10.1007/978-1-4757-5286-1_4
Publisher Name: Springer, Boston, MA
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Online ISBN: 978-1-4757-5286-1
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