针对农杆菌ATCC31749发酵法产凝胶多糖过程中产物质量浓度预测精度不高问题,提出一种基于模糊加权最小二乘支持向量机(least squares support vector machine,LSSVM)算法和机理模型相结合的混合建模新方法。首先通过添加模糊加权思想和...针对农杆菌ATCC31749发酵法产凝胶多糖过程中产物质量浓度预测精度不高问题,提出一种基于模糊加权最小二乘支持向量机(least squares support vector machine,LSSVM)算法和机理模型相结合的混合建模新方法。首先通过添加模糊加权思想和混合核函数方法对LSSVM算法进行优化,并用优化后的LSSVM求解农杆菌ATCC31749发酵过程动力学模型,结合鸟群算法对动力学模型参数进行寻优;然后拟合出溶氧体积分数和各参数之间的关联函数模型,并代入到动力学模型,建立起以溶氧浓度作为关键控制变量的发酵动力学模型;最后,用鸟群算法对模型进行寻优,寻找使得发酵产物浓度最大的最优溶氧过程控制策略。实验仿真结果表明,混合模型的预测精度得到提高,产多糖期溶氧体积分数控制为52%时,产物质量浓度最大,为48.85 g/L。该研究所建立的农杆菌发酵过程混合模型及其溶氧优化结果,为发酵工业上进一步通过最佳溶氧控制策略来提高多糖产量提供了方向。展开更多
Machine learning techniques are finding more and more applications in the field of load forecasting. A novel regression technique,called support vector machine (SVM),based on the statistical learning theory is applied...Machine learning techniques are finding more and more applications in the field of load forecasting. A novel regression technique,called support vector machine (SVM),based on the statistical learning theory is applied in this paper for the prediction of natural gas demands. Least squares support vector machine (LS-SVM) is a kind of SVM that has different cost function with respect to SVM. SVM is based on the principle of structure risk minimization as opposed to the principle of empirical risk minimization supported by conventional regression techniques. The prediction result shows that the prediction accuracy of SVM is better than that of neural network. Thus,SVM appears to be a very promising prediction tool. The software package NGPSLF based on SVM prediction has been put into practical business application.展开更多
文摘Machine learning techniques are finding more and more applications in the field of load forecasting. A novel regression technique,called support vector machine (SVM),based on the statistical learning theory is applied in this paper for the prediction of natural gas demands. Least squares support vector machine (LS-SVM) is a kind of SVM that has different cost function with respect to SVM. SVM is based on the principle of structure risk minimization as opposed to the principle of empirical risk minimization supported by conventional regression techniques. The prediction result shows that the prediction accuracy of SVM is better than that of neural network. Thus,SVM appears to be a very promising prediction tool. The software package NGPSLF based on SVM prediction has been put into practical business application.
