改进果蝇优化LSSVM超声波萃取产物浓度软测量

Soft measurement of ultrasonic extracted product concentration by LSSVM of improved fruit fly optimization algorithm

针对超声波天然产物萃取过程中产物浓度难以在线检测的问题,提出了一种改进果蝇优化最小二乘支持向量机(Least Squares Support Vector Machine,LSSVM)的超声波萃取产物浓度软测量建模方法。首先将混沌优化与迭代步长动态调节方法相融合,提出了一种混沌动态步长改进果蝇优化算法(Chaos Dynamic Step Fluit Fly Optimization Algorithm,CDSFOA),该算法引入动态调节因子对步长动态更新,并利用混沌优化实现各变量之间映射等操作,能够有效提高果蝇优化算法的收敛精度和收敛速度,然后利用CDSFOA对LSSVM进行参数寻优,构建最优CDSFOA-LSSVM软测量模型,最后利用超声波斛皮素萃取实验数据进行验证。结果表明,提出的模型不仅有较好的学习和泛化能力,而且具有良好的预测精度,可为超声波天然产物萃取工艺优化提供理论指导。

Aiming at the problem that the product concentration in ultrasonic natural product extraction is difficult to detect online,a soft measurement method using the least squares support vector machine(LSSVM),which is optimized by an improved fruit fly optimization algorithm(FOA),is proposed.Firstly,the improved FOA with chaotic dynamic step size(named as CDS-FOA)is obtained by combing chaos optimization and iterative step-size dynamic adjustment.This algorithm introduces the dynamic adjustment factor to update the step size dynamically,and uses chaos optimization to realize the mapping between different variables,which can effectively improve the convergence precision and convergence speed of the FOA.Then,the CDS-FOA is used to optimize the parameters of LSSVM to construct the optimal CDSFOA-LSSVM soft measurement model.Finally,the experimental data from ultrasonic quercetin extraction are used to verify the effectiveness.Results show that the proposed model not only has better learning and generalization ability,but also has good prediction accuracy,which can provide guidance for the optimization of ultrasonic natural product extraction processes.