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Learning in Feed-Forward Artificial Neural Networks I

Learning in Feed-Forward Artificial Neural Networks I

Lluís A. Belanche Muñoz
Copyright: © 2009 |Pages: 8
ISBN13: 9781599048499|ISBN10: 1599048493|EISBN13: 9781599048505
DOI: 10.4018/978-1-59904-849-9.ch148
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MLA

Belanche Muñoz, Lluís A. "Learning in Feed-Forward Artificial Neural Networks I." Encyclopedia of Artificial Intelligence, edited by Juan Ramón Rabuñal Dopico, et al., IGI Global, 2009, pp. 1004-1011. https://doi.org/10.4018/978-1-59904-849-9.ch148

APA

Belanche Muñoz, L. A. (2009). Learning in Feed-Forward Artificial Neural Networks I. In J. Rabuñal Dopico, J. Dorado, & A. Pazos (Eds.), Encyclopedia of Artificial Intelligence (pp. 1004-1011). IGI Global. https://doi.org/10.4018/978-1-59904-849-9.ch148

Chicago

Belanche Muñoz, Lluís A. "Learning in Feed-Forward Artificial Neural Networks I." In Encyclopedia of Artificial Intelligence, edited by Juan Ramón Rabuñal Dopico, Julian Dorado, and Alejandro Pazos, 1004-1011. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-59904-849-9.ch148

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Abstract

The view of artificial neural networks as adaptive systems has lead to the development of ad-hoc generic procedures known as learning rules. The first of these is the Perceptron Rule (Rosenblatt, 1962), useful for single layer feed-forward networks and linearly separable problems. Its simplicity and beauty, and the existence of a convergence theorem made it a basic departure point in neural learning algorithms. This algorithm is a particular case of the Widrow-Hoff or delta rule (Widrow & Hoff, 1960), applicable to continuous networks with no hidden layers with an error function that is quadratic in the parameters.

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