Redundancies in systems of fuzzy/linguistic IF-THEN rules
نویسندگان
چکیده
In this paper we will investigate which fuzzy/linguistic rules are redundant in systems of such rules called linguistic descriptions. We present a formal definition of redundancy and show that rules which are seemingly redundant can be in fact indispensable. These results apply for IF-THEN rules which use evaluative linguistic expressions (e.g., small, very big, etc.) and inference method called perception-based logical deduction (PbLD). However, they are also valid for inference systems which use compatible design choices with PbLD.
منابع مشابه
New results on redundancies of fuzzy/linguistic IF-THEN rules
In this paper we present new results on detection and removal of redundancies of IF-THEN rules in so-called linguistic descriptions (systems of such rules). We introduce an algorithm for removal of redundancies and describe a practical application.
متن کاملEffect of rule weights in fuzzy rule-based classification systems
This paper examines the effect of rule weights in fuzzy rule-based classification systems. Each fuzzy IF–THEN rule in our classification system has antecedent linguistic values and a single consequent class. We use a fuzzy reasoning method based on a single winner rule in the classification phase. The winner rule for a new pattern is the fuzzy IF–THEN rule that has the maximum compatibility gra...
متن کاملNew Applications on Linguistic Mathematical Structures and Stability Analysis of Linguistic Fuzzy Models
In this paper some algebraic structures for linguistic fuzzy models are defined for the first time. By definition linguistic fuzzy norm, stability of these systems can be considered. Two methods (normed-based & graphical-based) for stability analysis of linguist fuzzy systems will be presented. At the follow a new simple method for linguistic fuzzy numbers calculations is defined. At the end tw...
متن کاملPerformance evaluation of fuzzy classifier systems for multidimensional pattern classification problems
We examine the performance of a fuzzy genetics-based machine learning method for multidimensional pattern classification problems with continuous attributes. In our method, each fuzzy if-then rule is handled as an individual, and a fitness value is assigned to each rule. Thus, our method can be viewed as a classifier system. In this paper, we first describe fuzzy if-then rules and fuzzy reasoni...
متن کاملDesigning Highly Interpretable Fuzzy Rule-Based Systems with Integration of Expert and Induced Knowledge
This work describes a new methodology for fuzzy system modeling focused on maximizing the interpretability while keeping high accuracy. In order to get a good interpretability-accuracy trade-off, it considers the combination of both expert knowledge and knowledge extracted from data. Both types of knowledge are represented using the fuzzy logic formalism, in the form of linguistic variables and...
متن کامل