CAJ | 학술논문

支持向量机的核心是核函数,选择合适的核函数参数是支持向量机理论研究的重点.文中将遗传算法与Powell算法相结合,提出了GA-Powell算法来优化核函数的参数.首先利用遗传算法找到一个初始最优解,再利用Powell算法在所得解附近进行寻优,反复迭代产生最优解.该算法在保留遗传算法较强的全局搜索能力的同时具有Powell算法的较强的局部搜索能力,使得混合算法具有更加精确和快速的收敛性.将该算法应用到银行基金项目的分类实验中取得了良好的结果.
지지향량궤적핵심시핵함수,선택합괄적핵함수삼수시지지향량궤이론연구적중점.문중장유전산법여Powell산법상결합,제출료GA-Powell산법래우화핵함수적삼수.수선이용유전산법조도일개초시최우해,재이용Powell산법재소득해부근진행심우,반복질대산생최우해.해산법재보류유전산법교강적전국수색능력적동시구유Powell산법적교강적국부수색능력,사득혼합산법구유경가정학화쾌속적수렴성.장해산법응용도은행기금항목적분류실험중취득료량호적결과.
Support vector machine (SVM) is the core of the kernel function,selecting the proper parameters of kernel function are the fo-cus of support vector machine theory research. Combined GA and Powell algorithm,the GA-Powell algorithm was proposed to search op-timal parameters. First,use the genetic algorithm to find a initial solution,and then use the Powell algorithm,eventually produce the opti-mal solution. This method retained the global search capability of GA algorithm and the good local convergence of Powell,with more ac-curate and faster convergence. The algorithm is applied to the practice of bank found project classification and has good result.