The ultimate goal of control engineering is to implement an automatic system that could operate with increasing independence from human actions in an unstructured and uncertain environment. Such a system may be named autonomous or intelligent. It would need only to be presented with a goal and would achieve its objective by learning through continuous interaction with its environment through feedback about its behavior [13].
One class of models that has the capability to implement this learning is the artificial neural networks. Indeed, the neural morphology of the nervous system is quite complex to analyze. Nevertheless, simplified analogies have been developed, which could be used for engineering applications. Based on these simplified understandings, artificial neural networks are built [6].
An artificial neural network is a massively parallel distributed processor, inspired from biological neural networks, which can store experimental knowledge and makes it available for use. An artificial neural network consists of a finite number of neurons (structural element), which are interconnected to each other. It has some similarities with the brain, such as knowledge is acquired through a learning process and interneuron connectivity named as synaptic weights are used to store this knowledge, among others [13].
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(2008). Introduction. In: Discrete-Time High Order Neural Control. Studies in Computational Intelligence, vol 112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78289-6_1
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