Abstract
The human brain is like an information processing machine. Information can be seen as signals coming from senses, which then runs through the nervous system. The brain consists of around 100 to 500 billion neurons. These neurons form clusters and networks. Depending on their targets, neurons can be organized hierarchically or layered. In this chapter we present the basic model of the neuron in order to understand how neural networks work. Then, we offer the classification of these models by their structures and their learning procedure. Finally, we describe several neural models with their respective learning procedures. Some examples are given throughout the chapter.
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Hertz J, Krogh A, Lautrup B, Lehmann T (1997) Nonlinear backpropagation: doing backpropagation without derivatives of the activation function. Neural Networks, IEEE Transactions 8:1321–1327
Loh AP, Fong KF (1993) Backpropagation using generalized least squares. Neural Networks, IEEE International Conference 1:592–597
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(2010). Artificial Neural Networks. In: Intelligent Control Systems with LabVIEW™. Springer, London. https://doi.org/10.1007/978-1-84882-684-7_3
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DOI: https://doi.org/10.1007/978-1-84882-684-7_3
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