混沌最小二乘支持向量机及其在发酵过程建模中的应用

针对青霉素发酵过程的参数检测存在不确定因素，提出一种基于混沌最小二乘支持向量机的青霉素浓度预测方案。采用混沌优化算法对最小二乘支持向量机参数进行寻优，建立了一种混沌最小二乘支持向量机模型。首先，利用该模型对两种常规非线性函数曲线进行了仿真回归，结果表明，算法具有良好的建模精度；其次，基于Pensim仿真平台，运用文中方法预测青霉素发酵过程的产物量，实验仿真表明混沌优化算法具有良好的全局优化性能，在参数选择中可以有效避免陷入局部最小值，基于混沌优化的最小二乘支持向量机具有较高的建模精度。

Chaos least squares support vector machine and its application on fermentation process modeling

For uncertainties of parameter detection in penicillin fermentation process,penicillin concentration prediction scheme by chaos least squares support vector machine is put forward.The LSSVM parameters were optimized by chaos optimization algorithm to set up Chaos-LSSVM model. Firstly,simulation is conducted for two kinds of nonlinear function curve.The results show that the algorithm has good precision of modeling.Secondly,taking the data of Pensim simulation platform to model penicillin concentration curve,predicting the product of the penicillin fermentation process.The results show that chaos optimization algorithm has a good global optimization performance,which prevents parameters from falling into local minimum,improving the prediction accuracy of the model.