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The first chapter of part II of this book introduces various common learning
algorithms. The aim of chapter 6 is to acquaint the readers with the present-day
knowledge in learning paradigms. Filters may be employed in implementation of
learning algorithms, and vice versa. As such, the first few sections introduce Adaptive
Linear Neuron (ADALINE) and recursive Least-Square (RLS) algorithms. Artificial
intelligent systems may possess functional characteristics of living biological brain.
The multi-agent network and neuromorphic network introduced in subsequent sections
are examples of ANN systems with functional characteristics of living biological brain.
The ability of the brain to process data is unparalleled; the human research efforts have
however been able to discover a close match in Bayesian networks such that more than
half of this chapter is devoted to presenting various types of probability-density-based
learning algorithms. This is followed, in conclusion, by a hybrid neuro-fuzzy neural
network section. By reading this chapter, one may fully understand the common ANN
systems, and thus easily implement an ANN if required.
Adaptive Linear Neuron (ADALINE), Adaptive Network-based
Fuzzy Inference System (ANFIS), Agent, Capacitance, Efficacy, Expectation-
Maximization (E-M) algorithm, Generative Topographic Mapping (GTM),
Hodgkin-Huxley model, K-means, Knowledge base, Learning parameter,
Membrane potential, Mixture models, Nodes, Radial Basis Function (RBF),
Recursive Least-Square (RLS) algorithm, Sigmoid function, Sugeno-type Fuzzy
system, Synaptic current, Weight matrix.
University of Kent, United Kingdom.