Neural Networks in Multidimensional Domains: Fundamentals and New Trends in Modelling and Control

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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Bibliographische Detailangaben
Weitere beteiligte Personen: Arena, Paolo (HerausgeberIn), Fortuna, Luigi (HerausgeberIn), Muscato, Giovanni (HerausgeberIn), Xibilia, Maria Gabriella (HerausgeberIn)
Format: Elektronisch E-Book
Sprache:Englisch
Veröffentlicht: London Springer London 1998
Schriftenreihe:Lecture Notes in Control and Information Sciences 234
Schlagwörter:
Links:https://doi.org/10.1007/BFb0047683
https://doi.org/10.1007/BFb0047683
Zusammenfassung: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
Umfang:1 Online-Ressource (XIV, 169 p. 7 illus)
ISBN:9781846285271
DOI:10.1007/BFb0047683