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...

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Bibliographic Details

Other Authors:	Arena, Paolo (Editor), Fortuna, Luigi (Editor), Muscato, Giovanni (Editor), Xibilia, Maria Gabriella (Editor)
Format:	Electronic eBook
Language:	English
Published:	London Springer London 1998
Series:	Lecture Notes in Control and Information Sciences 234
Subjects:	Engineering Control Control engineering Mehrschichten-Perzeptron
Online Access:	BTU01 Volltext
Summary:	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
Physical Description:	1 Online-Ressource (XIV, 169 p. 7 illus)
ISBN:	9781846285271
DOI:	10.1007/BFb0047683