摘要
建立准确的非线性机理模型是发酵过程优化调控的关键。提出了一种基于改进粒子群优化算法(PSO,particle swarm optimization)的发酵过程模型参数估计方法,并将该方法用于青霉素发酵过程建模。改进的PSO算法通过引入粒子群能量对粒子进行自适应分群以防止粒子陷入局部最优,从而保证收敛于全局最优解。实验结果表明,该方法可以有效地实现青霉素发酵过程模型参数的准确估计,所得到的模型精度能够满足发酵过程的状态估计和控制需求。
Establishing an accurate nonlinear mechanism model is the key of optimal regulation in fermentation process. In this paper, a parameter estimation method of fermentation process model based on an improved particle swarm optimization （PSO） algorithm is proposed. And the method was used in penicillin fermentation process modeling. The particles in the improved PSO algorithm are partitioned into several sub-swarms adaptively according to the energy of the swarm to prevent the particles from going into a local optimum, thus ensuring that the algorithm converges to the global optimal solution. Experimental results show that the method can effectively realize accurate estimate of model parameters in penicillin fermentation process. The accuracy of the model can meet the requirements of state estimation and condition control in fermentation process.
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2010年第1期178-182,共5页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金(20476007
20676013)资助项目
关键词
粒子群算法
青霉素发酵
非线性模型
参数估计
particle swarm optimization
penicillin fermentation
nonlinear model
parameter estimation
