Fuzzy Support Vector Machine Based on Improved Sequential Minimal Optimization Algorithm
Qu Peixin@School of Information and Engineering Henan Institute of Science and Technology Henan Xinxiang 453003, China Wang Xianfang@School of Information and Control Engineering Jiangnan University Jiangsu Wuxi 214122, China Sequential minimal optimization (SMO) is quite an efficient algorithm for training the support vector machine, which can resolve the optimization question of a fuzzy support vector machine (FSVM), but it still requires a large amount of computation time for solving large size problems. This paper proposes an improved SMO using for fast training a fuzzy support vector machine. The training is realized by utilizing the inner loop and the outer loop until all examples obey KKT conditions. Using this method for modeling an actual penicillin fermentation process, the result shows that this method can not only make shorten training time, but also have a better predictive precision; on the other hand, this method can meet the request of process online survey. By analyzing the simulating result of the penicillin concentration, this method can advance the precision of simulating result affectively when the process parameters are changing greatly.
Fuzzy Support vector machine Sequential minimal optimization Inner loop outer loop KKT conditions
Du Zhiyong Dong Zuolin
Henan Mechanical and Electrical Engineering College Henan Xinxiang 453002, China
国际会议
成都
英文
152-155
2010-06-12（万方平台首次上网日期，不代表论文的发表时间）