Learning systems

Front Cover
Springer, 1995 - Computers - 119 pages
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.

From inside the book

What people are saying - Write a review

We haven't found any reviews in the usual places.

Contents

Introduction to Learning Systems
1
Deterministic Algorithms
16
Deterministic and Stochastic Algorithms of Optimisation
40
Copyright

6 other sections not shown

Other editions - View all

Common terms and phrases