The rapid growth of data and information confronting many in industry and business has created a need for extracting knowledge out of such data. Fuzzy logic is one of the techniques used within this area of computational and statistical tools. It has been a major commercial success, with many real-world applications including industrial control, medical and financial applications, and incorporation into consumer goods. Fuzzy sets generalise data and enable knowledge to be expressed as linguistic rules, giving a powerful and robust framework. This book discusses many existing applications and outlines the direction of future developments in fuzzy logic.
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Handling Priority and Preference in Constraint Satisfaction
Knowledge from Data using Fril and Fuzzy Methods
Mission Management System for Multiple Autonomous
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Adaptive algorithm application approach approximate reasoning architecture Artificial Intelligence associated function attributes Baldwin J.F. behaviour causal circuit classical CSPs classification clusters combination components Cons(P constraint satisfaction Constraint Satisfaction Problems customer segmentation database defined defuzzification diagnostic diagnostic reasoning domain dynamic error evidential logic rule evlog example FCSP Figure flexibility Fril fuzzy constraints fuzzy control Fuzzy Logic fuzzy relations fuzzy rules fuzzy set theory fuzzy subsets Fuzzy Systems global hypotheses IEEE implementation inference input variables instantiation Knowledge Based LEVEL SP perturbation linguistic Log Disp logic programming LogScale mass assignment membership functions membership values method Neural Networks neurofuzzy node null hypothesis operational amplifiers operator output parameters performed planning polytrees possibilistic possibility distribution Pr(C Pr(good Prade H predict prioritized constraints priority problem Proc quantifier represented rule base satisfies soft constraint strategy structure support pair symptoms techniques tion UD range Ulieru uncertainty vectors vehicles Zadeh L.A.
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