## Computing with WordsFuzzy logic refers to a computer's ability to make decisions involving "grey" or "fuzzy" areas. As linguistics contains numerous "grey" areas, computing with words through the use of fuzzy logic is an extremely hot topic in database and Internet research. This book explores the state of the art in linguistic computation, discussing how current research findings are extending the application of fuzzy logic beyond control engineering and intelligent systems into the use of language on a computer. Fuzzy logic pioneer, Dr. Lofti Zadeh, provides the introduction for this thought-provoking work. |

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Page 217

Since two target phrases remain and in our convention , each generalized rule

Since two target phrases remain and in our convention , each generalized rule

**corresponds**to only one target phrase , two rules are necessary ... If a training sentence has n important phrases , there are n**corresponding**rule entities ...Page 327

Construct primary conjunctions of the set symbols A and B

Construct primary conjunctions of the set symbols A and B

**corresponding**to linguistic values such that in a given row : ( a ) If a T appears , take the set affirmation symbol of that metalinguistic variable .Page 384

Suppose that we get a pair of transmitted signals s ( t ) and the

Suppose that we get a pair of transmitted signals s ( t ) and the

**corresponding**message signal m ( t ) , as shown in Figure 13.8a . Since spectral - temporal characteristics are more robust than temporal characteristics for ...### What people are saying - Write a review

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### Contents

FROM COMPUTING WITH NUMBERS TO COMPUTING WITH | 32 |

THE PROBLEMS | 69 |

GRANULAR RELATIONAL COMPUTING WITH SEMIOTIC | 85 |

Copyright | |

8 other sections not shown

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### Common terms and phrases

according adverbs agents algorithms allow analysis applications approach basic called chaotic cognitive communication complex computing with words concept considered constraints context corresponding decision defined Definition described domain dynamic elements entities environment equality example expert expressed extraction Figure formal function fuzzy logic fuzzy relations fuzzy set given granularity granulation human identified important inference intelligent internal interpretation knowledge learning linguistic mathematical meaning methods modified natural language objects observed operator particular perceptions performance phrase points possible precision presented principle probability problem procedures properties propositions query Question reasoning reference representation represented respectively rules semantic semiotic sense sentence shown shows signal situations space specific speech structures symbols Syst t-norms Table texts theory tion truth understanding University values variables verb weighted Zadeh