Learning Systems (Paperback, Edition.)

A learning system can be defined as a system which can adapt its behaviour to become more effective at a particular task or set of tasks. It consists of an architecture with a set of variable parameters and an algorithm. Learning systems are useful in many fields, one of the major areas being in control and system identification. This work covers major aspects of learning systems: system architecture, choice of performance index and methods measuring error. Major learning algorithms are explained, including proofs of convergence. Artificial neural networks, which are an important class of learning systems and have been subject to rapidly increasing popularity, are discussed. Where appropriate, examples have been given to demonstrate the practical use of techniques developed in the text. System identification and control using multi-layer networks and CMAC (Cerebellar Model Articulation Controller) are also presented.

General

Imprint:	Springer-Verlag
Country of origin:	Germany
Release date:	2001
First published:	1995
Authors:	Eduard Aved'yan
Editors:	J. Mason • P.C. Parks
Dimensions:	235 x 155 x 7mm (L x W x T)
Format:	Paperback
Pages:	119
Edition:	Edition.
ISBN-13:	978-3-540-19996-0
Categories:	Books > Computing & IT > General theory of computing > Mathematical theory of computation Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Books > Computing & IT > Applications of computing > Artificial intelligence > Neural networks
LSN:	3-540-19996-9
Barcode:	9783540199960

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