摘要
采用自适应算法调整粒子群的权重,优化非线性回归模型的参数,并将其应用于酶促反应的参数求解。与线性化、非线性最小二乘以及标准粒子群的结果比较表明,用自适应粒子群求解的非线性回归方程有更高的精度。
An adaptive weights adjustment algorithm for particle swarm optimization is adopted to optimize the parameters of nonlinear regression model in this paper. The adaptive particle swarm optimization is used to solve the parameters of enzyme catalytic reaction. Compared with linearation, nonlinear least square and standard particle swarm optimization, the results show that nonlinear regression equation based on adapative particle swarm optimization has higher precision.
出处
《武汉工业学院学报》
CAS
2010年第1期100-102,共3页
Journal of Wuhan Polytechnic University
基金
湖北省教育厅科研项目(Q20091809)
武汉工业学院基金资助项目(08Y30)
关键词
自适应
粒子群
非线性回归
adaptive
particle swarm optimization
nonlinear regression
