In this paper, the author research on electrical equipment’s fault diagnosis based on the improved support vector machine and fuzzy clustering. Combining the support vector combined fuzzy sets and neural network to carry on the fault diagnosis is a most prosperous diagnosis method. This article put forward a sample processing method using fuzzy clustering and studied the application of fuzzy co...

This paper describes a novel fuzzy rule-based modeling approach for some slow industrial processses. Structure identification is realized by clustering and support vector machines. When the process is slow, fuzzy rules can be obtained automatically. Parameters identification uses the techniques of fuzzy neural networks. A time-varying learning rate assures stability of the modeling error.

In order to classify data with noises or outliers, Fuzzy support vector machine (FSVM) improve the generalization power of traditional SVM by assigning a fuzzy membership to each input data point. In this paper, an improved FSVM based on vague sets is proposed by assigning a truthmembership and a false-membership to each data point. And we reformulate the improved FSVM so that different input p...

Fuzzy rule based classification systems is one of the most popular in pattern classification problems. The rules in the fuzzy models can be weighted to show the importance of generated rules where all attributes in the antecedent part of the rules have been usually weighted equally. Whereas the contributed attributes in a fuzzy model may have different influences on the decision making, a new m...

Recognizing and extracting exact name entities, like Persons, Locations, Organizations, Dates and Times are very useful to mining information from electronics resources and text. Learning to extract these types of data is called Named Entity Recognition (NER) task. Proper named entity recognition and extraction is important to solve most problems in hot research area such as Question Answering ...

Support vector machine (SVM) is one of effective biner classification technic with structural risk minimization (SRM) principle. SVM method known as successful in technic. But the real-life data problem lies occurrence noise and outlier. Noise will create confusion for when being processed. On this research, developed by adding its fuzzy membership function to lessen outlier effect trying figur...

In this paper, we formulate a least squares version of the one-class support vector fuzzy machine (LS one-class SVFM) which is combined with the fuzzy set theory. The parameters in the proposed algorithm, such as weight vector and bias term, are fuzzy numbers. Our model only needs to solve a system of linear equations, instead of a complex quadratic programming problem (QPP) solved in one-class...

This paper proposes support vector machine (SVM) based voice activity detection using FuzzyEn to improve detection performance under noisy conditions. The proposed voice activity detection (VAD) uses fuzzy entropy (FuzzyEn) as a feature extracted from noise-reduced speech signals to train an SVM model for speech/non-speech classification. The proposed VAD method was tested by conducting various...

In traditional fuzzy support vector machine(FSVM), membership function is established in global scope will reduce the membership of support vectors, and the FSVM based dismissing margin increases the training speed, but will remove some support vector artificially. So, a new algorithm of Fuzzy Support Vector Machine with Dual Membership based on Hypersphere (HDM-FSVM) is proposed. In this algor...