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A learning system can be defined as a system which can adapt its
behaviour to become more effective at a particular task or set of
tasks. It consists of an architecture with a set of variable
parameters and an algorithm. Learning systems are useful in many
fields, one of the major areas being in control and system
identification. This work covers major aspects of learning systems:
system architecture, choice of performance index and methods
measuring error. Major learning algorithms are explained, including
proofs of convergence. Artificial neural networks, which are an
important class of learning systems and have been subject to
rapidly increasing popularity, are discussed. Where appropriate,
examples have been given to demonstrate the practical use of
techniques developed in the text. System identification and control
using multi-layer networks and CMAC (Cerebellar Model Articulation
Controller) are also presented.
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