Neural Networks in Multidimensional Domains

Paolo Arena

ISBN 10: 1852330066 / ISBN 13: 9781852330064

Published by Springer London Apr 1998, 1998

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -In this monograph, new structures of neural networks in multidimensional domains are introduced. These architectures are a generalization of the Multi-layer Perceptron (MLP) in Complex, Vectorial and Hypercomplex algebra. The approximation capabilities of these networks and their learning algorithms are discussed in a multidimensional context. The work includes the theoretical basis to address the properties of such structures and the advantages introduced in system modelling, function approximation and control. Some applications, referring to attractive themes in system engineering and a MATLAB software tool, are also reported. The appropriate background for this text is a knowledge of neural networks fundamentals. The manuscript is intended as a research report, but a great effort has been performed to make the subject comprehensible to graduate students in computer engineering, control engineering, computer sciences and related disciplines. 184 pp. Englisch.

Seller Inventory # 9781852330064

Bibliographic Details

• Title: Neural Networks in Multidimensional Domains
• Publisher: Springer London Apr 1998
• Publication Date: 1998
• Language: English
• ISBN 10: 1852330066
• ISBN 13: 9781852330064
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

In this monograph, new structures of neural networks in multidimensional domains are introduced. These architectures are a generalization of the Multi-layer Perceptron (MLP) in Complex, Vectorial and Hypercomplex algebra. The approximation capabilities of these networks and their learning algorithms are discussed in a multidimensional context. The work includes the theoretical basis to address the properties of such structures and the advantages introduced in system modelling, function approximation and control. Some applications, referring to attractive themes in system engineering and a MATLAB software tool, are also reported. The appropriate background for this text is a knowledge of neural networks fundamentals. The manuscript is intended as a research report, but a great effort has been performed to make the subject comprehensible to graduate students in computer engineering, control engineering, computer sciences and related disciplines.