Membership function

About: Membership function is a research topic. Over the lifetime, 15795 publications have been published within this topic receiving 418366 citations.

Complete design of fuzzy logic systems using genetic algorithms

Abstract: The paper presents a general method for constructing accurate high-dimensional fuzzy logic systems (FLSs). Generally, the design of FLSs involves determination of the number of fuzzy rules, the structure of the rules, and membership function parameters. Most techniques treat these parts separately, which may result in a suboptimal solution. We propose to optimize all three parts simultaneously using genetic algorithm (GA) techniques. While GAs are very robust with respect to avoiding local minima, they can be slow in refining the solution once near the optimum. Thus, the FLS obtained from GA search is further fine-tuned using a conjugate gradient method. The advantages of the proposed method are demonstrated through a comparison with other fuzzy modeling techniques and feedforward neural networks on modeling a nonlinear dynamic system, and industrial process. >

Step-by-Step Fuzzy Diagnosis Method for Equipment Based on Symptom Extraction and Trivalent Logic Fuzzy Diagnosis Theory

Abstract: A step-by-step fuzzy diagnostic method based on frequency-domain symptom extraction and trivalent logic fuzzy diagnosis theory (TLFD), which is established by combining the trivalent logic inference theory with the possibility and fuzzy theories, is proposed herein. The features for diagnosing a number of abnormal states are extracted sequentially from the measured signals using statistical tests in the frequency domain. The symptom parameters (SPs) that can sensitively reflect symptoms of abnormal states are then selected to provide effective information for the discrimination of each state. The membership function of each state is then generated based on the possibility theory using the probability functions of the SPs. The step-by-step fuzzy diagnoses are performed based on the TLFD. This method can be used extensively to diagnose anomalies in various equipment. In this study, the diagnosis of structure faults of a rotating machine is cited as an example to demonstrate the effectiveness and universality of this method.

A type-2 fuzzy C-means clustering algorithm

Abstract: This paper presents a type-2 fuzzy C-means (FCM) algorithm that is an extension of the conventional fuzzy C-means algorithm. In our proposed method, the membership values for each pattern are extended as type-2 fuzzy memberships by assigning membership grades to the type-1 memberships. In doing so, cluster centers that are estimated by type-2 memberships may converge to a more desirable location than cluster centers obtained by a type-1 FCM method in the presence of noise. Experimental results are given to show the effectiveness of our method.