fuzzy logic is one of the concepts that has created different scientific attitudes by entering into various professional fields nowadays and in some cases has made remarkable effects on the results of the practical researches. however, the existence of stochastic and uncertain situations in risk and accident field, affects the possibility of the forecasting and preventing the occurrence of the ...

A generalized probability mixture density governs an additive fuzzy system. The fuzzy system’s if-then rules correspond to the mixed probability densities. An additive fuzzy system computes an output by adding its fired rules and then averaging the result. The mixture’s convex structure yields Bayes theorems that give the probability of which rules fired or which combined fuzzy systems fired fo...

i n troduction: cancer is a major cause of mortality in the modern world, and one of the most important health problems in societies. during recent years, research on cancer as a system biology disease is focused on molecular differences between cancer cells and healthy cells. most of the proposed methods for classifying cancer using gene expression data act as black boxes and lack biological i...

Nowadays, fuzzy rule-based systems are successfully applied to many different real-world problems. Unfortunately, relatively few well-structured methodologies exist for designing and, in many cases, human experts are not able to express the knowledge needed to solve the problem in the form of fuzzy rules. Takagi-Sugeno-Kang (TSK) fuzzy rule-based systems were enunciated in order to solve this d...

In the paper, logical axioms, inference rules, semantics, and some specific properties including the completeness theorems of four kinds of fuzzy type theory are presented. This theory is a higher order fuzzy logic that can be used for precise formalization, for example, of the theory of computing with words, fuzzy IF-THEN rules, approximate reasoning, and others.

This paper presents a new boosting algorithm for genetic learning of fuzzy classification rules. The method is based on the iterative rule learning approach to fuzzy rule base system design. The fuzzy rule base is built in an incremental fashion, in that the evolutionary algorithm extracts one fuzzy classifier rule at a time. The boosting mechanism reduces the weight of those training instances...

Fuzzy logic control is being increasingly applied to solve control problems in areas where system complexity, development time and cost are the major issues. In the absence of a system mathematical model , a fuzzy system model is described which is analogous to a human operator’s behavior, based on approximate reasoning bound by a minimum set of rules. A set of linguistic fuzzy control rules ar...

In this paper we present a new learning method for rule-based feed-forward and recurrent fuzzy systems. Recurrent fuzzy systems have hidden fuzzy variables and can approximate the temporal relation embedded in dynamic processes of unknown order. The learning method is universal i.e. it selects optimal width and position of Gaussian like membership functions and it selects a minimal set of fuzzy...

The design of controllers for nonlinear systems in industry is a complex and difficult task. One approach which has shown promise for solving nonlinear control problems is the use of fuzzy logic control. This paper proposes a new method utilizing proportional–integral-derivative (PID) control as a hybrid fuzzy PID controller for nonlinear system. The salient feature of the proposed approach is ...

The problem of sparse fuzzy rule bases is introduced. Because of the high computational complexity of the original compositional rule of inference (CRI) method, it is strongly suggested that the number of rules in a final fuzzy knowledge base is drastically reduced. Various methods of analogical reasoning available in the literature are reviewed. The mapping style interpretation of fuzzy rules ...