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Learning Automata and Stochastic Optimization (Lecture Notes in Control and Information Sciences)

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In the last decades there has been a steadily growing need and interest in computational methods for solving optimization problems with or without constraints. They play an important role in many fields (chemistry, mechanic, electrical, economic, etc.). Optimization techniques have been gaining greater acceptance in many industrial applications. This fact was motivated by the increased interest for improved economy in and better utilization of the existing material resources. Euler says: "Nothing happens in the universe that does not have a sense of either certain maximum or minimum". In this book we are primarily concerned with the use of learning automata as a tool for solving many optimization problems. Learning systems have made a significant impact on many areas of engineering problems including modelling, control, optimization, pattern recognition, signal processing and diagnosis. They are attractive and provide interesting methods for solving complex nonlinear problems characterized by a high level of uncertainty. Learning systems are expected to provide the capability to adjust the probability distribution on-line, based on the environment response. They are essentially feedback systems. The optimization problems are modeled as that of learning automaton or a hierarchical structure of learning automata operating in a random environment. W~e report new and efficient techniques to deal with different kinds (unconstrained, constrained) of stochastic optimization problems. The main advantage of learning automata over other optimization techniques is its general applicability, i.e., there are almost no condition concerning the function to be optimized (continuity, differentiability, convexity, unimodality, etc.).

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.

Deals with stochastic optimization problems and provides an introduction to learning automata. Covers unconstrained optimization problems, constrained optimization problems and optimization of nonstationary functions. Paper.
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Fuzzy Probabilities: New Approach and Applications (Studies in Fuzziness and Soft Computing)
Fuzzy Probabilities: New Approach and Applications (Studies in Fuzziness and Soft Computing)
In probability and statistics we often have to estimate probabilities and parameters in probability distributions using a random sample. Instead of using a point estimate calculated from the data we propose using fuzzy numbers which are constructed from a set of confidence intervals. In probability calculations we apply constrained fuzzy arithmetic...
Granular Computing: At the Junction of Rough Sets and Fuzzy Sets (Studies in Fuzziness and Soft Computing)
Granular Computing: At the Junction of Rough Sets and Fuzzy Sets (Studies in Fuzziness and Soft Computing)
Since their very inception, both fuzzy and rough set theories have earned a sound, well-deserved reputation owing to their intrinsic capabilities to model uncertainty coming from the real world. The increasing amount of investigations on both subjects reported every year in the literature vouches for the dynamics of the area and its rapid...
Combinatorial Group Theory: Presentations of Groups in Terms of Generators and Relations
Combinatorial Group Theory: Presentations of Groups in Terms of Generators and Relations
This seminal, much-cited account begins with a fairly elementary exposition of basic concepts and a discussion of factor groups and subgroups. The topics of Nielsen transformations, free and amalgamated products, and commutator calculus receive detailed treatment. The concluding chapter surveys word, conjugacy, and related problems; adjunction...

Fuzzy Algorithms for Control (International Series in Intelligent Technologies)
Fuzzy Algorithms for Control (International Series in Intelligent Technologies)

Fuzzy Algorithms for Control gives an overview of the research results of a number of European research groups that are active and play a leading role in the field of fuzzy modeling and control. It contains 12 chapters divided into three parts.

Chapters in the first part address the position of fuzzy systems in control...

Mathematical Programming: Theory and Methods
Mathematical Programming: Theory and Methods
This book is a result of my teaching mathematical programming to graduate students of the University of Delhi for over thirty years. In preparing this book, a special care has been made to see that it is self-contained and that it is suitable both as a text and as a reference.

The book is divided in three parts.
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Stochastic Optimal Control and the U.S. Financial Debt Crisis
Stochastic Optimal Control and the U.S. Financial Debt Crisis

Stochastic Optimal Control (SOC)—a mathematical theory concerned with minimizing a cost (or maximizing a payout) pertaining to a controlled dynamic process under uncertainty—has proven incredibly helpful to understanding and predicting debt crises and evaluating proposed financial regulation and risk...

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