粒子群与遗传算法优化支持向量机的应用
|
摘要
为了提高参数优化精度,研究将粒子群算法与支持向量机相结合,建立基于粒子群算法的支持向量机复杂过程系统稳态模型。在此基础上,为解决粒子群算法容易出现早熟收敛、搜索精度不高、在迭代的后期效率低、容易陷入局部极优点等问题,提出了引入遗传算法的改进粒子群算法。通过利用改进后的粒子群算法对支持向量机参数进行优化,并应用到青霉素发酵这一复杂工业系统。仿真结果表明,改进算法提高了工业产量,实现了对系统结果的优化。
In order to improve the precision of the parameter optimization,the research integrates the Particle Swarm Optimization Algorithm with Support Vector Machine,and matches the experimental data,and then establishes a steady-state model of complex process system,which is based on Particle Swarm Optimization Algorithm and Support Vector Machine. On the basis of this model,an improved Particle Swarm Optimization Algorithm introduced to Genetic Algorithm is proposed,in order to overcome the defects of Particle Swarm Optimization Algorithm about premature convergence-searching accuracy is not high,the iterative efficiency is low in the late stage,trapping into the local optimization and so on. By making use of the improved Particle Swarm Optimization algorithm to optimize the parameters of Support Vector Machine,it is applied to the complex industrial system of penicillin fermentation. The simulation result shows that the optimized algorithm improves the industrial outputs,and optimizes the system results.
In order to improve the precision of the parameter optimization,the research integrates the Particle Swarm Optimization Algorithm with Support Vector Machine,and matches the experimental data,and then establishes a steady-state model of complex process system,which is based on Particle Swarm Optimization Algorithm and Support Vector Machine. On the basis of this model,an improved Particle Swarm Optimization Algorithm introduced to Genetic Algorithm is proposed,in order to overcome the defects of Particle Swarm Optimization Algorithm about premature convergence-searching accuracy is not high,the iterative efficiency is low in the late stage,trapping into the local optimization and so on. By making use of the improved Particle Swarm Optimization algorithm to optimize the parameters of Support Vector Machine,it is applied to the complex industrial system of penicillin fermentation. The simulation result shows that the optimized algorithm improves the industrial outputs,and optimizes the system results.
引文
[1] TOM F. Moving Beyond Steady State Optimization. Hydrocarbon Processing,Jun 2002,81(6):15.
[2]张鑫源,胡晓敏,林盈.遗传算法和粒子群优化算法的性能对比分析[J].计算机科学与探索,2014,8(1):91.
[3]韦琦,杨明.改进神经网络算法的智能电网短期负荷预测[J].哈尔滨理工大学学报,2017,(04):65.
[4] DEAN T,ALLEN J,ALOIMONOS Y. Artificial Intelligence:Theory and Practice. Pearson Education North Asia and Publishing House of Electronics Industry,2003:17.
[5]曹春红,张永坚,李文辉.杂交粒子群算法在工程几何约束求解中的应用.仪器仪表学报,2004,8(8):397.
[6]陈君,彭小奇,宋彦坡,等.特征空间最小数据分布最大间隔支持向量机[J].电机与控制学报,2013(3):105.
[7]赵娟平,陈健,姜长洪青霉素发酵过程建模研究[J],计算机仿真,2008,55(9):25.
[8]李国正,王猛,曾华正.支持向量机导论[M].北京:电子工业出版社,2004.
[9]潘丰,李海波,吕光帅.发酵过程的建模与优化方法研究.计算机与应用化学2006,23(11):1112.
[10]盖晁旭,梁隆恺,何勇军.数据不充分情况下的说话人识别[J].哈尔滨理工大学学报,2017(3):13.
[11] ZHANG X G. Introduciton to Statistical Learning Theory and Support Vector Machine. Acta Automatic Since,2000,26(l):32.
[12]周建萍,郑应平,王志萍.最小二乘支持向量机分类问题的算法实现[J].上海电力学院学报,2008,24(4):381.
[13]杨冬云,李数函.支持向量机核函数的构造方法研究与分析[J].高师理科学刊,2010,30(2):23.
[14] KENNEDY J,EBERHART R. Particle Swarm Optimization. In:Proc IEEE Int Conf on Neural Networks,1995:1942.
[15]马志伟,满春涛,李巍.基于STDM与粒子群优化的音频水印算法[J].哈尔滨理工大学学报,2016(5):73.
[16]熊伟丽,徐保国.粒子群算法在支持向量机参数选择优化中的应用研究[C]∥中国控制与决策学术年会论文集,沈阳:东北大学出版社,2007:447.
[17] WANG Xibin,ZHANG Xiaoping,WANG Hanhu. Parameter Optimization of Support Vector Machine and Application Based on Particle Swarm Optimization Mode Search. Journal of Computer Applications,2011.
[18]马超,邓超,熊尧,等.一种基于混合遗传和粒子群的智能优化算法[J].计算机研究与发展,2013(11).
[19]王广泽.改进粒子群算法在供应链库存控制中的应用[J].哈尔滨理工大学学报,2016(5):90.
[20]朱霄珣,徐搏超,焦宏超,等.遗传算法对SVR风速预测模型的多参数优化[J].电机与控制学报,2017,21(2):70.
[2]张鑫源,胡晓敏,林盈.遗传算法和粒子群优化算法的性能对比分析[J].计算机科学与探索,2014,8(1):91.
[3]韦琦,杨明.改进神经网络算法的智能电网短期负荷预测[J].哈尔滨理工大学学报,2017,(04):65.
[4] DEAN T,ALLEN J,ALOIMONOS Y. Artificial Intelligence:Theory and Practice. Pearson Education North Asia and Publishing House of Electronics Industry,2003:17.
[5]曹春红,张永坚,李文辉.杂交粒子群算法在工程几何约束求解中的应用.仪器仪表学报,2004,8(8):397.
[6]陈君,彭小奇,宋彦坡,等.特征空间最小数据分布最大间隔支持向量机[J].电机与控制学报,2013(3):105.
[7]赵娟平,陈健,姜长洪青霉素发酵过程建模研究[J],计算机仿真,2008,55(9):25.
[8]李国正,王猛,曾华正.支持向量机导论[M].北京:电子工业出版社,2004.
[9]潘丰,李海波,吕光帅.发酵过程的建模与优化方法研究.计算机与应用化学2006,23(11):1112.
[10]盖晁旭,梁隆恺,何勇军.数据不充分情况下的说话人识别[J].哈尔滨理工大学学报,2017(3):13.
[11] ZHANG X G. Introduciton to Statistical Learning Theory and Support Vector Machine. Acta Automatic Since,2000,26(l):32.
[12]周建萍,郑应平,王志萍.最小二乘支持向量机分类问题的算法实现[J].上海电力学院学报,2008,24(4):381.
[13]杨冬云,李数函.支持向量机核函数的构造方法研究与分析[J].高师理科学刊,2010,30(2):23.
[14] KENNEDY J,EBERHART R. Particle Swarm Optimization. In:Proc IEEE Int Conf on Neural Networks,1995:1942.
[15]马志伟,满春涛,李巍.基于STDM与粒子群优化的音频水印算法[J].哈尔滨理工大学学报,2016(5):73.
[16]熊伟丽,徐保国.粒子群算法在支持向量机参数选择优化中的应用研究[C]∥中国控制与决策学术年会论文集,沈阳:东北大学出版社,2007:447.
[17] WANG Xibin,ZHANG Xiaoping,WANG Hanhu. Parameter Optimization of Support Vector Machine and Application Based on Particle Swarm Optimization Mode Search. Journal of Computer Applications,2011.
[18]马超,邓超,熊尧,等.一种基于混合遗传和粒子群的智能优化算法[J].计算机研究与发展,2013(11).
[19]王广泽.改进粒子群算法在供应链库存控制中的应用[J].哈尔滨理工大学学报,2016(5):90.
[20]朱霄珣,徐搏超,焦宏超,等.遗传算法对SVR风速预测模型的多参数优化[J].电机与控制学报,2017,21(2):70.