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.
| ISBN: | 9781852330064 |
| Publication date: | 28th April 1998 |
| Author: | Paolo Arena |
| Publisher: | Springer an imprint of Springer London |
| Format: | Paperback |
| Pagination: | 165 pages |
| Series: | Lecture Notes in Control and Information Sciences |
| Genres: |
Automatic control engineering |
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.
Neural Networks in Multidimensional Domains features in the following genres: Automatic control engineering
Paperback. Not Available.
Neural Networks in Multidimensional Domains was written by Paolo Arena and published by Springer an imprint of Springer London
Neural Networks in Multidimensional Domains has 165 pages
Yes it is part of Lecture Notes in Control and Information Sciences series