摘要
发酵动力学模型参数估计是基于最小二乘的参数估计问题,是根据满足最小二乘的标准来解决模型的最佳参数匹配,求解该问题可采用遗传算法,但简单遗传算法容易陷入局部最优。本文提出一种自适应域多群体遗传算法,它通过自调整参数域,避免陷入局部最优,同时还提高搜索到的解是全局最优解的可靠性,适用于很多领域的应用优化问题。
The parameter estimation of the fermentation kinetics model is an estimation problem based on least-squares,and resolves the optimal parameters matching of the model based on least squares criteria.But the simple genetic algorithm is easy to fall into the local optimal.This paper presents a Adaptive Domain Method With Multiple Genetic Algorithms.It avoids falling into local optimum through self-adjusting the parameter domain,and also improves the reliability that the solution searched is global optimal solution,and is applicable to applied optimization problems in many areas.
出处
《计算技术与自动化》
2010年第3期18-23,共6页
Computing Technology and Automation
关键词
自适应域
遗传算法(GA)
发酵
参数估计
adaptive domain
genetic algorithm（GA）
fermentation
parameter estimation
