Learning rules for a fuzzy inference model

Learning rules for a fuzzy inference model*

" The problem of learning rules for a fuzzy inference model directly from empirical observations, without resorting to assessments from experts is considered. We develop a method that builds uncertain rules from a set of examples. These rules match the following pattern: If X is A then Y is B is [a,/3], where A and B are fuzzy sets representing fuzzy restrictions on the variables X and Y; a and...

A Learning Process for Fuzzy Control Rules

The purpose of this paper is to present a genetic learning process for learning fuzzy control rules from examples. It is developed in three stages: the rst one is a fuzzy rule genetic generating process based on a rule learning iterative approach, the second one combines two kinds of rules, experts rules if there are and the previously generated fuzzy control rules, removing the redundant fuzzy...

A QUADRATIC MARGIN-BASED MODEL FOR WEIGHTING FUZZY CLASSIFICATION RULES INSPIRED BY SUPPORT VECTOR MACHINES

Recently, tuning the weights of the rules in Fuzzy Rule-Base Classification Systems is researched in order to improve the accuracy of classification. In this paper, a margin-based optimization model, inspired by Support Vector Machine classifiers, is proposed to compute these fuzzy rule weights. This approach not only  considers both accuracy and generalization criteria in a single objective fu...

Quadrature Rules for Fuzzy Integrals

Developing a Risk Management Model for Banking Software Development Projects Based on Fuzzy Inference System

Risk management is one of the most influential parts of project management that has a major impact on the success or failure of projects. Due to the increasing use of information technology (IT) systems in all fields and the high failure rate of IT projects in software development and production, it is essential to effectively manage these projects is essential. Therefore, this study is aimed t...