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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.
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.
University of Kent, United Kingdom.