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By: (Edited by) Bogdan M. Wilamowski , (Edited by) J. David Irwin
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As intelligent systems continue to replace and sometimes outperform human intelligence in decision-making processes and solution of very complex problems, computational intelligence has branched out in several directions. This volume covers applications of intelligent systems, including artificial neural networks that can learn how to classify patterns, such as images or sequences of events, and effectively model complex nonlinear systems. It also explores simple and easy-to-implement fuzzy systems for modeling and system control. This section of the handbook illustrates how these and other tools help engineers model nonlinear system behavior, determine and evaluate system parameters, and ensure overall system control.
The Industrial Electronics Handbook, Second Edition combines traditional and newer, more specialized knowledge that will help industrial electronics engineers develop practical solutions for the design and implementation of high-power applications. Embracing the broad technological scope of the field, this collection explores fundamental areas, including analog and digital circuits, electronics, electromagnetic machines, signal processing, and industrial control and communications systems. It also facilitates the use of intelligent systems—such as neural networks, fuzzy systems, and evolutionary methods—in terms of a hierarchical structure that makes factory control and supervision more efficient by addressing the needs of all production components.
Enhancing its value, this fully updated collection presents research and global trends as published in the IEEE Transactions on Industrial Electronics Journal, one of the largest and most respected publications in the field.
As intelligent systems continue to replace and sometimes outperform human intelligence in decision-making processes, they have made substantial contributions to the solution of very complex problems. As a result, the field of computational intelligence has branched out in several directions. For instance, artificial neural networks can learn how to classify patterns, such as images or sequences of events, and effectively model complex nonlinear systems. Simple and easy to implement, fuzzy systems can be applied to successful modeling and system control.
Illustrating how these and other tools help engineers model nonlinear system behavior, determine and evaluate system parameters, and ensure overall system control, Intelligent Systems:
Other volumes in the set:
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