Membership function

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

A new measure using intuitionistic fuzzy set theory and its application to edge detection

Abstract: In this paper, a new attempt has been made using Attanassov's intuitionistic fuzzy set theory for image edge detection. Intuitionistic fuzzy set takes into account the uncertainty in assignment of membership degree known as hesitation degree. Also a new distance measure, called intuitionistic fuzzy divergence, has been proposed. With this proposed distance measure, edge detection is carried out, and the results are found better with respect to the previous methods.

A weighted fuzzy reasoning algorithm for medical diagnosis

Abstract: This paper presents a weighted fuzzy reasoning algorithm for handling medical diagnostic problems, where fuzzy set theory and fuzzy production rules are used for knowledge representation. The algorithm can perform fuzzy matching between the patient's symptom manifestations and the antecedent portions of fuzzy production rules to determine the presence of diseases, where the result is interpreted as a certainty level indicating the degree of certainty of the presence of the disease. Because the algorithm allows each symptom in medical diagnosis to have a different degree of importance, it is more flexible than the ones we presented in [3] and [4]. The algorithm can be executed very efficiently. If the knowledge base contains n fuzzy production rules and there are p symptoms, then the time complexity of the algorithm is O ( np ).

Finding Features for Real-Time Premature Ventricular Contraction Detection Using a Fuzzy Neural Network System

Abstract: Fuzzy neural networks (FNNs) have been successfully applied to generate predictive rules for medical or diagnostic data. This brief presents an approach to detect premature ventricular contractions (PVCs) using the neural network with weighted fuzzy membership functions (NEWFMs). The NEWFM classifies normal and PVC beats by the trained bounded sum of weighted fuzzy membership functions (BSWFMs) using wavelet transformed coefficients from the MIT-BIH PVC database. The eight generalized coefficients, locally related to the time signal, are extracted by the nonoverlap area distribution measurement method. The eight generalized coefficients are used for the three PVC data sets with reliable accuracy rates of 99.80%, 99.21%, and 98.78%, respectively, which means that the selected features are less dependent on the data sets. It is shown that the locations of the eight features are not only around the QRS complex that represents ventricular depolarization in the electrocardiogram (ECG) containing a Q wave, an R wave, and an S wave, but also the QR segment from the Q wave to the R wave has more discriminate information than the RS segment from the R wave to the S wave. The BSWFMs of the eight features trained by NEWFM are shown visually, which makes the features explicitly interpretable. Since each BSWFM combines multiple weighted fuzzy membership functions into one using the bounded sum, the eight small-sized BSWFMs can realize real-time PVC detection in a mobile environment.