Fuzzy Expert Systems and Fuzzy ReasoningAnnotation Fuzzy sets were for a long time not accepted by the AI community. Now they have become highly evolved and their techniques are well established. This book will teach the reader how to construct a fuzzy expert system to solve real-world problems. After a general discussion of expert systems, the basic fuzzy math required is presented first, requiring little more math background than high-school algebra. This book will fill a void in the market because although there are many books on expert systems, none devote more than a few pages to the notion of fuzzy sets and their applications in this domain. Therefore their use in this book is timely and should be well received. The book is designed as a text and has ample problems with solutions, a solutions manual and an accompanying program on our ftp site. Coverage is accessible to practitioners and academic readers alike. |
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antecedent confidence approximate reasoning block 1 goal cann classical logic classification command mode consequent crisp data declarations data element debugging debugging commands default defined defuzzification delete discrete fuzzy set downward monotonic Fact fact Figure fire block fireable rules FLOPS program fuzzify fuzzy control fuzzy control systems fuzzy expert system fuzzy logic fuzzy number fuzzy propositions fuzzy reasoning fuzzy relation fuzzy set members grade of membership hypothesis implication operator inference input data Iris Klir and Yuan language linguistic terms linguistic variable Medium membership functions method min-max modified modus ponens monotonic reasoning newly fireable non-monotonic reasoning output OutputError parallel pconf pconf 1000 rule PetalL problem prstack r0 Time Tags Region frame reset response rnum rule block rule firing rule goal rule r0 rule r2 rule rule rconf rule-based rule-firing SepalL STACK BOTTOM STACK TOP stimulus t-norm TFLOPS truth value tv(P write Zadehian zero