Artificial Neural Systems: Principle and Practice

Neural Networks

Author(s): Pierre Lorrentz

Pp: 118-153 (36)

DOI: 10.2174/9781681080901115010009

* (Excluding Mailing and Handling)


This chapter describe various types of ANN systems in relative detail. It is the aim of the chapter to give descriptions of advanced ANN system in such a detail as to facilitate easy implementation. The first few sections are dedicated to the recent weightless neural networks. This is followed by a weighted neural system section. Two advanced Bayesian network are introduced subsequently. The last section of the chapter explains the dynamics of ANN and how ANN nay be evaluated. The chapter has given a relatively extensive description of typical advanced neural networks from various categories of ANN systems.

Keywords: Adjustment, Back-propagation, Boltzmann distribution, Conditional probability, Division, Enhanced Probabilistic Convergent Network (EPCN), Generalized Likelihood Ration Test (GLRT), Helmholtz Machine, Kernel function, Kullback-Leibler divergence, Merging, Minimum Description Length, Mixture Density Network (MDN), Multi-classifier, Multi-expert System, Multi- Layered Perceptron (MLP), Probabilistic Convergent Network (PCN), Random Access Memory (RAM), Squared error, Wald test.

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