In the last decade there has been a steadily growing need for and interest in computational methods for solving stochastic optimization problems with or wihout constraints. Optimization techniques have been gaining greater acceptance in many industrial applications, and learning systems have made a significant impact on engineering problems in many areas, including modelling, control, optimization, pattern recognition, signal processing and diagnosis. Learning automata have an advantage over other methods in being applicable across a wide range of functions. Featuring new and efficient learning techniques for stochastic optimization, and with examples illustrating the practical application of these techniques, this volume will be of benefit to practicing control engineers and to graduate students taking courses in optimization, control theory or statistics.

Bibliographic Information

Book Title: Learning Automata and Stochastic Optimization
Authors: A. S. Poznyak, K. Najim
Series Title: Lecture Notes in Control and Information Sciences
DOI: https://doi.org/10.1007/BFb0015102
Publisher: Springer Berlin, Heidelberg
eBook Packages: Springer Book Archive
Softcover ISBN: 978-3-540-76154-9Published: 12 March 1997
eBook ISBN: 978-3-540-40938-0Published: 11 December 2005
Series ISSN: 0170-8643
Series E-ISSN: 1610-7411
Edition Number: 1
Number of Pages: XII, 207
Number of Illustrations: 6 b/w illustrations
Topics: Control, Robotics, Mechatronics, Vibration, Dynamical Systems, Control, Complex Systems, Statistical Physics and Dynamical Systems

Publish with us

Policies and ethics

Learning Automata and Stochastic Optimization

Overview

Access this book

Other ways to access

Table of contents (6 chapters)

Front Matter

Introduction

Stochastic optimization

On learning automata

Unconstrained optimization problems

Constrained optimization problems

Optimization of nonstationary functions

Back Matter

Keywords

About this book

Bibliographic Information

Publish with us

Search

Navigation