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Neural Networks

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Robust and Fault-Tolerant Control

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 197))

Abstract

This chapter is devoted to the presentation of neural-network models in the context of control systems design. It is divided into four parts. The first two parts introduce the reader to the theory of static and dynamic neural network structures. These parts can be treated as a quick review of already developed and well-documented neural network architectures, giving an insight into their properties and the possibility of their application in control theory. The third part is focused on the problem of model design. As the majority of control system designs are model based, developing an accurate model of a plant is of crucial importance, especially for nonlinear systems. Two modelling approaches are discussed: forward and inverse modelling. Moreover, the problem of a training of feed-forward and recurrent neural models is described in the context of parallel and series-parallel identification schemes. The fourth part discusses a very important issue of uncertainty associated with the model. This notion is crucial when dealing with robust and fault-tolerant control. We describe the methods that could be used in estimating the uncertainty associated with neural network models, namely the set-membership identification, model error modelling and statistical approaches.

Portions of the chapter reused by permission from Springer Nature, Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes by Krzysztof Patan \(\copyright \)2008.

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  1. Institute of Control and Computation Engineering, University of Zielona Góra, Zielona Góra, Poland

    Krzysztof Patan

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Patan, K. (2019). Neural Networks. In: Robust and Fault-Tolerant Control. Studies in Systems, Decision and Control, vol 197. Springer, Cham. https://doi.org/10.1007/978-3-030-11869-3_2

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