CAJ | 학술논문

为解决青霉素发酵过程预测建模中存在的输入变量选择问题，提出了基于核目标度量（ kernel target alignment，KTA）和最小二乘支持向量机（least squares support vector machines，LSSVM）的青霉素发酵过程预测模型。首先，在分析影响青霉素产物浓度相关因素的基础上选取输入变量，采用KTA对输入变量进行尺度缩放，然后，利用 Pensim 仿真平台数据，采用混沌粒子群算法对 LSSVM 的参数寻优，建立青霉素发酵过程的KTA-LSSVM预测模型。青霉素浓度预测的KTA-LSSVM模型均方根误差为0.0179，LSSVM模型的均方根误差为0.0276，实验结果表明，本文提出的模型预测精度高，推广性能好。
위해결청매소발효과정예측건모중존재적수입변량선택문제，제출료기우핵목표도량（ kernel target alignment，KTA）화최소이승지지향량궤（least squares support vector machines，LSSVM）적청매소발효과정예측모형。수선，재분석영향청매소산물농도상관인소적기출상선취수입변량，채용KTA대수입변량진행척도축방，연후，이용 Pensim 방진평태수거，채용혼돈입자군산법대 LSSVM 적삼수심우，건립청매소발효과정적KTA-LSSVM예측모형。청매소농도예측적KTA-LSSVM모형균방근오차위0.0179，LSSVM모형적균방근오차위0.0276，실험결과표명，본문제출적모형예측정도고，추엄성능호。
A new prediction method of penicillin fed-batch fermentation based on kernel target alignment (KTA) and least squares support vector machines (LSSVM) was proposed to deal with input variable selection. Firstly,input variables were selected by analyzing the factors which affected penicillin concentration,then,the KTA feature rescaling method was used to rescale input variables. Finally,the simulation data from Pensim simulation platform was used to establish the LSSVM model in the penicillin fed-batch fermentation by using chaos particle swarm optimization (CPSO) on the LSSVM parameters optimization. The root mean square error (RMSE) of the proposed method was 0.0179,whereas the RMSE of LSSVM was 0.0276,showing better prediction and generalization.