Artificial Neural Systems:: Principle and Practice by Pierre Lorrentz

Author:Pierre Lorrentz [Lorrentz, Pierre]
Language: eng
Format: azw3, epub
ISBN: 9781681080918
Publisher: Bentham Science Publishers
Published: 2016-02-10T16:00:00+00:00

INTRODUCTION

In sections 1 and 2, some weightless neural networks are described in considerable detail. Weightless networks are presented here because they form a good alternative to weighted classical neural networks and also less prone to noise. A stereo-type weighted network, the Multi-Layered Perceptron (MLP), is described in the third section. The MLP is included because of its robustness and popularity; it represents a good example of weighted neural networks. Section four describes more advanced types of Bayesian classifier. They are suitably introduced here because the usual types of Bayesian classifiers have been described in chapter 6. The last section of chapter 7 presents the dynamics of an ANN system, and discussed the fusion mechanism of hierarchical network. This is followed by methods of independent evaluation of ANN systems.

The first and second sections of chapter 7 show ANN systems whose learning and recognition algorithms are derived from Boolean logic.. The PCN and EPCN may be employed in selection mechanism described in chapter 8. Seeking minimal sets of weights by MLP may be synonymous to seeking a set of (minimal) basis functions. Whatever the structural architecture of MLP that has been determined may be implemented by using the classical primitives (gates) of chapter 5. The MLP can be used as a component neural network of neuro-fuzzy system of chapter 6. The probability theories of chapters 2 may have provided sufficient background principle to the Bayesian networks of the fourth section of chapter 7. Any of the Bayesian network may participate in the selection mechanism of chapter 8. The last section of this chapter may be regarded as a continuation of performance evaluation methods that has been introduced in chapter 4. The performance evaluation mechanism of the last section is algorithmic and in considerable detail, whereas that of chapter 4 is introductory. Most ANN systems of other chapters may be evaluated for performance by using the methods of the last section of chapter 7.

Chapter 7 has described large number of standard ANN systems in considerable detail.

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