Reduced-Support-Vector-Based Fuzzy-Neural Model with Application to the Material Property Prediction

摘要

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 IFȁ3;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 pre images 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.