Abstract

The purpose of this chapter is to introduce a powerful class of mathematical models: the artificial neural networks. This is a very general term that includes many different mathematical models and various types of approaches, both from statistics and computer science. Our aim is not to examine them all (it would be a very long discussion), but to understand the basic functionality and the possible implementations of this powerful tool. We initially introduce networks, by analogy with the human brain. The analogy is not very detailed, but it serves to introduce the concept of parallel and distributed computing. Then we analyze in detail a widely applied type of artificial neural network: the feed-forward network with error back- propagation algorithm. We illustrate the architecture of the models, the main learning methods and data representation. The final section deals with a series of applications and extensions to the basic model.

Autore Pugliese

• Gallo Crescenzio

Tutti gli autori

• C. Gallo , M. Perilli , M. De Bonis

Titolo volume/Rivista

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Anno di pubblicazione

2014

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ISBN

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Numero di citazioni Wos

Nessuna citazione

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Numero di citazioni Scopus

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Settori ERC

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Codici ASJC

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