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Control and Dynamic Systems: Advances in Theory and Application, Volume 26: System Identification and Adaptive Control, Part 2 of 3 deals with system parameter identification and adaptive control. It presents useful techniques for effective stochastic adaptive control systems. This volume presents a powerful technique for identifying discrete time and continuous time linear time-invariant multivariable systems. It also includes the use of identifiable representations for linear multivariable systems; parametric identification of transfer functions of linear system; compares model reference adaptive control and model identification control; estimation of transfer function models; multivariable self-tuning control; and covariance analysis. This volume ends with powerful techniques for adaptive control for stochastic linear systems. This text is of great value to practitioners in the field who want a comprehensive reference source of techniques with significant applied implications.
Control and Dynamic Systems: Advances in Theory and Applications, Volume 42: Analysis and Control System Techniques for Electric Power Systems, Part 2 of 4 covers the research studies on the significant advances in areas including economic operation of power systems and voltage and power control techniques. This book is composed of eight chapters and begins with a survey of the application of parallel processing to power system analysis as motivated by the requirement for faster computation. The next chapters deal with the issues of power system protection from a system point of view, the voltage stability phenomenon, and an overview of the techniques used in the reliability evaluation of large electric power systems. These chapters also look into the reliability assessment of bulk power systems, which are the composite of generation and high-voltage transmission, often called composite systems. These topics are followed by investigations of the potential of integer quadratic optimization to improve efficiency in a radial electric distribution system through the coordination of switched capacitors and regulators. Other chapters consider the issues of the optimal operation of a power system that are substantially complicated as a result of the large system scale nature of these issues. The final chapters explore the techniques for achieving requisite speed improvements that are essential to electric power systems and the problems on effective methods in hydro optimization. This book will be of value to electrical engineers, designers, and researchers.
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Artificial neural networks and genetic algorithms both are areas of research which have their origins in mathematical models constructed in order to gain understanding of important natural processes. By focussing on the process models rather than the processes themselves, significant new computational techniques have evolved which have found application in a large number of diverse fields. This diversity is reflected in the topics which are subjects of the contributions to this volume. There are contributions reporting successful applications of the technology to the solution of industrial/commercial problems. This may well reflect the maturity of the technology, notably in the sense that 'real' users of modelling/prediction techniques are prepared to accept neural networks as a valid paradigm. Theoretical issues also receive attention, notably in connection with the radial basis function neural network. Contributions in the field of genetic algorithms reflect the wide range of current applications, including, for example, portfolio selection, filter design, frequency assignment, tuning of nonlinear PID controllers. These techniques are also used extensively for combinatorial optimisation problems.
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