Our concern is with the determination of the firing level of the antecedent fuzzy set in a fuzzy systems model rule base. We first consider the case where the input information is also expressed in terms of a normal fuzzy set. We provide the requirements needed by any formulation of this operation. We next consider the case when the input information is expressed using a measure. Here we also p...

to monitor the patient behavior, data are collected from patient’s body by a medical monitoring device so as to calculate the output using embedded software. incorrect calculations may endanger the patient’s life if the software fails to meet the patient’s requirements. accordingly, the veracity of the software behavior is a matter of concern in the medicine; moreover, the data collected from t...

Generalizing our earlier results on optimization with linguistic variables [3, 6, 7] we introduce a novel statement of fuzzy multiobjective mathematical programming problems and provide a method for finding a fair solution to these problems. Suppose we are given a multiobjective mathematical programming problem in which the functional relationship between the decision variables and the objectiv...

Generalizing our earlier results on optimization with linguistic variables [3, 6, 7] we introduce a novel statement of fuzzy multiobjective mathematical programming problems and provide a method for findig a fair solution to these problems. Suppose we are given a multiobjective mathematical programming problem in which the functional relationship between the decision variables and the objective...

In the 3eld of fuzzy modelling, the exclusive consideration of the modelling error leads to problems concerning the handling of high-dimensional applications and the interpretability of the resulting rule base. To solve those problems, a statistically motivated fuzzy rule test is proposed. It decides if a fuzzy IF=THEN statement is a relevant rule or not. In this way, the problem of 3nding a go...

This paper presents genetic algorithms (GAs) to perform the optimal design of an auto-tuning fuzzy proportional-integral-derivative (PID) controller and to determine the minimal number of fuzzy rules simultaneously. Different from PID controllers with fixed gains, the fuzzy PID controller is expressed in terms of fuzzy rules, in which the input variables are the error signals and their derivati...

A new kind of mapping rule base scheme is proposed to get the fuzzy rules of hierarchical fuzzy systems. The algorithm of this scheme is developed such that one can easily design the involved fuzzy rules in the middle layers of the hierarchical structure. In contrast with the conventional single layer fuzzy controller, the present method has approximate performance using the same scaling factor...

The main goal of this work is to study the performance of CARFIR (Automatic Construction of Rules in Fuzzy Inductive Reasoning) methodology for the modelling and prediction of the human central nervous system (CNS). The CNS controls the hemodynamical system by generating the regulating signals for the blood vessels and the heart.CARFIR is able to automatically construct fuzzy rules starting fro...

Sophisticated fuzzy rule systems are supposed to be flexible, complete, consistent and compact (FC(3)). Flexibility, and consistency are essential for fuzzy systems to exhibit an excellent performance and to have a clear physical meaning, while compactness is crucial when the number of the input variables increases. However, the completeness and consistency conditions are often violated if a fu...

Most fuzzy controllers and fuzzy expert systems must predefine membership functions and fuzzy inference rules to map numeric data into linguistic variable terms and to make fuzzy reasoning work. In this paper, we propose a general learning method as a framework for automatically deriving membership functions and fuzzy if-then rules from a set of given training examples to rapidly build a protot...