Fuzzy Logic for Embedded Systems Applications
Fuzzy Logic for Embedded Systems Applications, by a recognized expert in the field, covers all the basic theory relevant to electronics design, with particular emphasis on embedded systems, and shows how the techniques can be applied to shorten design cycles and handle logic problems that are tough to solve using conventional linear techniques. All the latest advances in the field aree discussed and practical circuit design examples presented.
Fuzzy logic has been found to be particularly suitable for many embedded control applications. The intuitive nature of the fuzzy-based system design saves engineers time and reduces costs by shortening product development cycles and making system maintenance and adjustments easier. Yet despite its wide acceptance—and perhaps because of its name—it is still misunderstood and feared by many engineers. There is a need for embedded systems designers—both hardware and software—to get up to speed on the principles and applications of fuzzy logic in order to ascertain when and how to use them appropriately.
Fuzzy Logic for Embedded Systems Applications provides practical guidelines for designing electronic circuits and devices for embedded systems using fuzzy-based logic. It covers both theory and applications with design examples.
* Unified approach to fuzzy electronics from an engineering point of view
* Easy to follow with plenty of examples
* Review and evaluation of free resources
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2 Fuzzy Sets
3 Fuzzy Relations
4 Embedded Fuzzy Applications
5 Critique of Fuzzy Logic
6 Neural Networks
7 Hybrid Systems
8 Hardware Implementations
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analysis architecture artificial neural networks backpropagation basic Bibliography binary bounded difference CD-ROM Product Chapter chip cluster Computing connectionism control system crisp set current mirror defined defuzzification demo DETERGENT DIRTINESS discussed downloading Electronics Elsevier Science embedded systems Engineering Example Let Fuzzy Control fuzzy control system fuzzy inference Fuzzy Logic Controller Fuzzy Neural Networks fuzzy relations fuzzy rules fuzzy set Fuzzy Systems fuzzyTECH genetic algorithms hardware Hopfield Hopfield network IEEE IEEE Transactions Implementation of Fuzzy input patterns Integrated Circuits Introduction Java layer leads learning linguistic mathematical MATLAB membership function membership values method microcontrollers microprocessors MOSFET multilayer perceptron Neuro-Fuzzy operation optimization paper PDF format PID controller problem processor provides links referred self-organizing Set Theory Sets and Systems shown in Figure signal simulation source code techniques TOOLBOX Topics transistors tutorial vector VLSI voltage weights µ µ µ