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This paper proposes a classification algorithm based on simplified fuzzy rules base combining fuzzy clustering with rough set. Firstly, generates fuzzy rules base using fuzzy clustering from numerical sample dates, and then simplifies the sample attributions using rough set theory, deletes the redundant rules, and gets the simplified fuzzy rules base, in order to make classification decision conveniently...
Learning fuzzy rule-based systems with genetic algorithms can lead to very useful descriptions of several optimization and search problems. In the fuzzy logic method, when the inputs to the fuzzy controller in any process are increased, then the number of rules increases exponentially. To overcome the above problem, genetic algorithm (GA) is used. Genetic algorithm is a search and optimization technique...
The method was studied about traffic flow prediction by using subtractive clustering for fuzzy neural network model of phase-space reconstruction. The prediction model of traffic flow must be established to satisfy the intelligent need of high precision through analyzing problems of the existing predicting methods in chaos traffic flow time series and the demand of uncertain traffic system. Based...
This article introduces and evaluates a fuzzy logic based representation for HTML document clustering using Self-Organizing Maps. This representation is built on heuristic combinations of criteria by means of a fuzzy rules system and based on the HTML markup. We evaluate the model using different feature vector sizes. Experimental results show an improvement in clustering quality when the fuzzy logic-based...
Information retrieve is one of the most important operations in computer information systems. This paper presents a kind of fuzzy information retrieve method based on soft computing (SCFIR for short). SCFIR adopts fuzzy clustering analysis and artificial neural networks to organize databases in systems so as to increase the efficiency of fuzzy retrieve. At the same time, SCFIR realizes the understanding...
Fuzzy modeling is an effective approach for system identification. It is based on fuzzy sets and logic and describes the system behaviour by means of fuzzy IF-THEN rules. In its turn, data driven fuzzy modeling (DDFM) extracts these models from a set of input-output observations about the system. Three main stages compose DDFM: rules number identification, rules generation and parameter optimization...
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