Learning in Feed-Forward Artificial Neural Networks II

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Author(s): Lluís A. Belanche Muñoz (Universitat Politècnica de Catalunya, Spain)
Copyright: 2009
Pages: 6
Source title: Encyclopedia of Artificial Intelligence
Source Author(s)/Editor(s): Juan Ramón Rabuñal Dopico (University of A Coruña, Spain), Julian Dorado (University of A Coruña, Spain)and Alejandro Pazos (University of A Coruña, Spain)
DOI: 10.4018/978-1-59904-849-9.ch149

Keywords: Artificial Intelligence / Computer Science & IT / Information Science Reference

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Abstract

Supervised Artificial Neural Networks (ANN) are information processing systems that adapt their functionality as a result of exposure to input-output examples. To this end, there exist generic procedures and techniques, known as learning rules. The most widely used in the neural network context rely in derivative information, and are typically associated with the Multilayer Perceptron (MLP). Other kinds of supervised ANN have developed their own techniques. Such is the case of Radial Basis Function (RBF) networks (Poggio & Girosi, 1989). There has been also considerable work on the development of adhoc learning methods based on evolutionary algorithms.

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