This paper proposes two kinds of fuzzy abductive inference in the framework of fuzzy rule base. The abductive inference processes described here depend on the semantic of the rule. We distinguish two classes of interpretation of a fuzzy rule, certainty generation rules and possible generation rules. In this paper we present the architecture of abductive inference in the first class of interpret...

This paper proposes a new novel method for the online construction of a Hierarchical Fuzzy Rule Based System (FRBS) to accurately model a function while retaining a level of human interpretability. The algorithm uses an information theoretic approach to limit the amount of uncertainty within each decision and to determine when a rule does not effectively model the underlying decision space. Exp...

In natural language processing up to now mainly rule-based systems have been used. The rulebased approach has been successful for limited domains, where the manual construction of the rules is feasible. In recent studies, however, a need for automatic generation of the rules has been seen. On the other hand, when children learn their mother tongue no explicit rules seem to be needed. Therefore,...

In the last years, multi-objective genetic algorithms have been successfully applied to obtain Fuzzy Rule-Based Systems satisfying different objectives, usually different performance measures. Recently, multi-objective genetic algorithms have been also applied to improve the difficult trade-off between interpretability and accuracy of Fuzzy Rule-Based Systems, obtaining linguistic models not on...

Images of fuzzy sets under fuzzy relations have been investigated mainly in two contexts: On the one hand, mostly under the term “full image” [5], they can be regarded as very general tools for fuzzy inference, leading to the so-called “compositional rule of inference” [1, 5]. On the other hand, under the term “extensional hull”, the image of a fuzzy set under a fuzzy equivalence relation yield...

On this paper, an application of a high performance fuzzy logic hardware controller is explained, a model car is given autonomous character. Development of a fuzzy rule base, aided by a simulator program, is followed by trials made to come to conclusions regarding ideal and real functioning of the hardware developed.

We describe the foundations of a system for rule-based programming which integrates two powerful mechanisms: (1) matching with context variables, sequence variables, and regular constraints for their matching values; and (2) strategic programming with labeled rules. The system is called ρLog, and is built on top of the pattern matching and rule-based programming capabilities of Mathematica.

Recently researchers have suggested several computational models in which, one programs by specifying large networks of simple devices. Such models are interesting because they go to the roots of concurrency the circuit level. A problem with the models is that it is unclear how to program large systems and expensive to implement many features that are taken for granted m symbolic programming la...

In the approximate fuzzy reasoning the covering over of fuzzy rule base input and rule premise of a rule determines the importance of that fuzzy rule and the rule output as well. An axiom system has been created, describing the relationship between the fuzzy rule base system, rule input and rule output. By using distance-based operators a novel reasoning method appears by the compositional rule...