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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.
Dealing with digital filtering methods for 1-D and 2-D signals,
this book provides the theoretical background in signal processing,
covering topics such as the z-transform, Shannon sampling theorem
and fast Fourier transform. An entire chapter is devoted to the
design of time-continuous filters which provides a useful
preliminary step for analog-to-digital filter conversion.
This book contains more than 150 problems and solutions on the control of linear continuous systems. The main definitions and theoretical tools are summarized at the beginning of each chapter, after which the reader is guided through the problems and how to solve them. The author provides coverage of the ideas behind the developments of the main PID tuning techniques, as well as presenting the proof of the Routh-Hurwitz stability criterion and giving some new results dealing with the design of root locus.
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