【作者】 王欣； 阳春华； 秦斌；

【Author】 Wang Xin~(1,2) Yang Chunhua~2 Qin Bin~1 (1 School of Electric and Information Engineering,Hunan University of Technology Zhuzhou 412008 China; 2 School of Information Science and engineering Central South University Changsha 411083 China)

【机构】 湖南工业大学电气与信息工程学院； 中南大学信息科学与工程学院；

【摘要】 针对模糊系统应用中模糊规则较难获取的问题,提出了模糊规则的一种新的学习算法,首先将模糊模型转化为RBF网络,而采用径向基函数核的支持向量机可以看作一种特殊的RBF网络,通过粒子群算法优化支持向量机参数,最终获得模糊模型。将提出的方法应用到高炉热风炉专家系统规则获取中,取得了良好的应用效果。结果表明,与RBF神经网络模型方法相比,该方法可提高模糊规则的精度。更多还原

【Abstract】 A new learning algorithm for fuzzy rules based on support vector machines(SVM) is presented.Firstly a fuzzy model is changed to RBF network and SVM with RBF kernel can be taken as a kind of special RBF network.Secondly the parameters of SVM are optimized by using particle swarm optimization(PSO) algorithm. Finally an optimized fuzzy model is obtained.The experiments result shows that the hitting rate of predicting the silicon content and the accuracy of fuzzy rules are increased in proposed method comparing with RBF neural network model.更多还原