Download Fuzzy Control and Modeling: Analytical Foundations and by Hao Ying PDF

By Hao Ying

The rising, strong fuzzy keep an eye on paradigm has resulted in the global luck of numerous advertisement items and real-world purposes. Fuzzy keep watch over is phenomenally useful and cost effective as a result of its particular skill to complete initiatives with out figuring out the mathematical version of the approach, whether it really is nonlinear, time various and intricate. however, in comparison with the traditional keep an eye on expertise, such a lot fuzzy keep watch over functions are constructed in an advert hoc demeanour with little analytical knowing and with no rigorous procedure research and design.Fuzzy keep watch over and Modeling is the one e-book that establishes the analytical foundations for fuzzy keep an eye on and modeling when it comes to the traditional linear and nonlinear theories of keep watch over and structures. The insurance is updated, accomplished, in-depth and rigorous. Numeric examples and functions illustrate the software of the theoretical development.Important issues mentioned comprise: buildings of fuzzy controllers/models with appreciate to standard fuzzy controllers/models research of fuzzy regulate and modeling relating to their classical counterpartsStability research of fuzzy structures and layout of fuzzy regulate systemsSufficient and valuable stipulations on fuzzy structures as common approximatorsReal-time fuzzy keep watch over structures for therapy of life-critical difficulties in biomedicineFuzzy regulate and Modeling is a self-contained, valuable source for pros and scholars in various technical fields who aspire to analytically examine fuzzy regulate and modeling.

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Extra info for Fuzzy Control and Modeling: Analytical Foundations and Applications (IEEE Press Series on Biomedical Engineering)

Example text

In theory, these variables can be either continuous or discrete; practically speaking, however, they should be discrete ~ec~us~ virtually ~ll fuzzy controllers and models are implemented using digital comyuters. A, B, C, D, and E are fuzzy sets, and AND are fuzzy logic AND operators. "IF Xl is A AND Xl is lJ AND X3 is C" is called the rule antecedent, whereas the remaining part is named the rule consequent. The structure of Mamdani fuzzy rules for fuzzy modeling is the same. The variables involved, however, are different.

FUZZY RULES A fuzzy controller or model uses fuzzy rules, which are linguistic if-then statements involving fuzzy sets, fuzzy logic, and fuzzy inference. Fuzzy rules playa key role in representing expert control/modeling knowledge and experience and in linking the input variables of fuzzy controllers/models to output variable (or variables). Two major types of fuzzy rules exist, namely, Mamdani fuzzy rules and Takagi-Sugeno (TS, for short) fuzzy rules [202]. 1. Mamdani Fuzzy Rules A simple but representative Mamdani fuzzy rule describing the movement of a car is: IF Speed is High AND Acceleration is Small THEN Braking is (should be) Modest, where Speed and Acceleration are input variables and Braking is an output variable.

As stated in Chapter 1, the most widely used types of input fuzzy sets are triangular, trapezoidal, Gaussian, and bell-shaped. Regardless of the shape, input fuzzy sets are usually required to be so positioned that (1) any two adjacent membership functions overlap once and (2) at any sampling time, two nonzero membership values are yielded by fuzzification of each input variable. The rationale behind the requirements is to limit the number of fuzzy rules executed at any time to an adequate level.

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