## Fuzzy Reasoning in Information, Decision and Control SystemsS.G. Tzafestas, Anastasios N. Venetsanopoulos Great progresses have been made in the application of fuzzy set theory and fuzzy logic. Most remarkable area of application is 'fuzzy control', where fuzzy logic was first applied to plant control systems and its use is expanding to consumer products. Most of fuzzy control systems uses fuzzy inference with max-min or max-product composition, similar to the algorithm that first used by Mamdani in 1970s. Some algorithms are developed to refine fuzzy controls systems but the main part of algorithm stays the same. Triggered by the success of fuzzy control systems, other ways of applying fuzzy set theory are also investigated. They are usually referred to as 'fuzzy expert sys tems', and their purpose are to combine the idea of fuzzy theory with AI based approach toward knowledge processing. These approaches can be more generally viewed as 'fuzzy information processing', that is to bring fuzzy idea into informa tion processing systems. |

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### Contents

CHAPTER | 3 |

Generalization of the Three Fundamental Operations | 11 |

Some Basic Theorems and Principles | 17 |

Copyright | |

57 other sections not shown

### Other editions - View all

Fuzzy Reasoning in Information, Decision and Control Systems S.G. Tzafestas,Anastasios N. Venetsanopoulos No preview available - 2013 |

### Common terms and phrases

algorithm application of fuzzy approach approximate reasoning certainty rules computed Conference on Fuzzy connectionist expert system considered control action control loop control rules control variable controller output corresponding crisp decision defined defuzzification denotes domain dynamic Engineering error estimation evaluation example feature Figure fuzzy control systems Fuzzy Identifier fuzzy implication fuzzy inference fuzzy logic controller fuzzy network fuzzy numbers fuzzy reasoning fuzzy relation fuzzy rules fuzzy set theory fuzzy subset fuzzy systems given IEEE Trans implementation Intelligent knowledge base knowledge representation linear linguistic variables Logic and Neural Mamdani matrix medium membership functions membership values method neural network neuron nodes non-linear obtained operations parameters pattern class performance Petri net PID controller possibility distribution Prade problem procedure relational equation represented respectively rule base rule-based sample set Sets and Systems sliding mode control T-norm Table uncertainty universe of discourse vector Zadeh