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Introduction to Fuzzy Logic using MATLAB

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The world we live in is becoming ever more reliant on the use of electronics and computers to control the behavior of real-world resources. For example, an increasing amount of commerce is performed without a single banknote or coin ever being exchanged. Similarly, airports can safely land and send off airplanes without ever looking out of a window. Another, more individual, example is the increasing use of electronic personal organizers for organizing meetings and contacts. All these examples share a similar structure; multiple parties (e.g., airplanes or people) come together to co-ordinate their activities in order to achieve a common goal. It is not surprising, then, that a lot of research is being done into how a lot of mechanics of the co-ordination process can be automated using computers.

Fuzzy logic means approximate reasoning, information granulation, computing with words and so on.

Ambiguity is always present in any realistic process. This ambiguity may arise from the interpretation of the data inputs and in the rules used to describe the relationships between the informative attributes. Fuzzy logic provides an inference structure that enables the human reasoning capabilities to be applied to artificial knowledge-based systems. Fuzzy logic provides a means for converting linguistic strategy into control actions and thus offers a high-level computation.

Fuzzy logic provides mathematical strength to the emulation of certain perceptual and linguistic attributes associated with human cognition, whereas the science of neural networks provides a new computing tool with learning and adaptation capabilities. The theory of fuzzy logic provides an inference mechanism under cognitive uncertainty, computational neural networks offer exciting advantages such as learning, adaptation, fault tolerance, parallelism, and generalization.
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