Artificial Intelligence and Soft Computing: Behavioral ... - Arteimi.info

14
Machine Learning
Using Neural Nets
The chapter presents various types of supervised, unsupervised and
reinforcement learning models built with artificial neural nets. Among the
supervised models special emphasis has been given to Widrow-Hoff’s multilayered
ADALINEs and the back-propagation algorithm. The principles of
unsupervised learning have been demonstrated through Hopfield nets, and the
adaptive resonance theory (ART) network models. The reinforcement learning
is illustrated with Kohonen’s self-organizing feature map. The concepts of
fuzzy neural nets will also be introduced in this chapter to demonstrate its
application in pattern recognition problems.
14.1 Biological Neural Nets
The human nervous system consists of small cellular units, called neurons.
These neurons when connected in tandem form nerve fiber. A biological
neural net is a distributed collection of these nerve fibers.
A neuron receives electrical signals from its neighboring neurons,
processes those signals and generates signals for other neighboring neurons
attached to it. The operation of a biological neuron, which decides the nature
of output signal as a function of its input signals is not clearly known to date.