The nature of statistical learning theory
The nature of statistical learning theory
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Support vector machines based on K-means clustering for real-time business intelligence systems
International Journal of Business Intelligence and Data Mining
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
A systematic neuro-fuzzy modeling framework with application tomaterial property prediction
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Support vector learning mechanism for fuzzy rule-based modeling: a new approach
IEEE Transactions on Fuzzy Systems
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
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A fuzzy model based on support vector regression (SVR) and particle swarm optimization (PSO) for the property prediction of heat treatment process of alloy steels is presented in this paper. First, a SVR model is built and the parameters of SVR are optimized by using the grid optimization algorithm. a set of equivalent fuzzy IF-THEN rules is generated from the obtained support vectors, then PSO is utilized to obtain a optimal fuzzy model with reduced rule(support vector) which approximate preimages of the original SVR model. The proposed modeling approach has been used for the mechanical property prediction in hot-rolled steels. Preliminary results reveal that the proposed modelling approach can lead to accurate and flexible fuzzy models.