Consistency checking for fuzzy expert systems

Checking an Expert Systems Knowledge Base for Consistency and Completeness

In t h i s paper we desor ibe a program t h a t v e r i f i e s t he oons is tenoy and completeness of exper t system knowledge bases whioh u t i l i z e t he Lookheed Expert System (LES) framework. The a l g o r i t h m s desor ibed here are not s p e c i f i c to LES and oan be a p p l i e d to most r u l e -based systems. The program, o a l l e d CHECK, combines l o g i o a l p r i n c i p l...

Fuzzy Expert Systems

In this Chapter, the steps necessary to develop a fuzzy expert system (FES) from the initial model design through to final system evaluation will be presented. The current state-of-the-art of fuzzy modelling can be summed up informally as 'anything goes'. What this actually means is that the developer of the fuzzy model is faced with many steps in the process each with many options from which s...

Knowledge acquisition for fuzzy expert systems

Expert systems have been successfully applied to a wide variety of application domains. To achieve better performance, researchers have tried to employ fuzzy logic to the development of expert systems. However, as fuzzy rules and membership functions are difficult to define, most of the existing tools and environments for expert systems do not support fuzzy representation and reasoning. Thus, i...

Fuzzy Estimators in Expert Systems

In this paper we consider the use of fuzzy estimators –a new and promising approach of estimating the parameters of a probability distribution from statistical samples– to represent mathematical knowledge in expert systems. A class of fuzzy estimators suitable for fuzzy arithmetics generalizes the existing approaches and it is used to derive the fuzzy estimators for the parameters of the normal...

Fuzzy situational inference for expert systems with fuzzy logic