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.
作者
满春涛
刘博
曹永成
MAN Chun-tao;LIU Bo;CAO Yong-cheng(School of Automation,Harbin University of Science and Technology,Harbin 150080,China)
出处
《哈尔滨理工大学学报》
CAS
北大核心
2019年第3期87-92,共6页
Journal of Harbin University of Science and Technology
基金
黑龙江省教育厅科学技术研究项目(12521092)
关键词
粒子群优化算法
遗传算法
支持向量机
青霉素发酵
particle swarms optimization algorithm
genetic algorithm
support vector machine
penicillin fermentation
