CAJ | 학술논문

针对汽轮机热耗率难以准确计算的问题，提出了核模糊 c 均值与混合蛙跳算法优化最小二乘支持向量机（LS-SVM）的汽轮机热耗率多模型建模方法，用来计算不同工况下的热耗率。该方法利用核模糊 c 均值算法对热耗率数据聚类，采用5折交叉验证平均误差作为 LS-SVM 参数选择的适应度值，利用混合蛙跳算法优化参数并建立局部模型，采用开关切换得到模型输出，以此实现热耗率的多模型建模。与单一的 LS-SVM 模型和 BP 网络热耗率预测模型比较，结果表明该多模型方法有更高的预测精确和更好的泛化能力，能更准确地计算汽轮机热耗率。
침대기륜궤열모솔난이준학계산적문제，제출료핵모호 c 균치여혼합와도산법우화최소이승지지향량궤（LS-SVM）적기륜궤열모솔다모형건모방법，용래계산불동공황하적열모솔。해방법이용핵모호 c 균치산법대열모솔수거취류，채용5절교차험증평균오차작위 LS-SVM 삼수선택적괄응도치，이용혼합와도산법우화삼수병건립국부모형，채용개관절환득도모형수출，이차실현열모솔적다모형건모。여단일적 LS-SVM 모형화 BP 망락열모솔예측모형비교，결과표명해다모형방법유경고적예측정학화경호적범화능력，능경준학지계산기륜궤열모솔。
Taking into account the problem that the heat rate of steam turbine is difficult to accurately calculate,a novel heat rate multi-model soft measurement methodology based on kernel fuzzy c-means and shuffled frog-leaping algorithm optimized least squares support vector machine(LS-SVM is proposed),which is employed to calculate the heat rate under different working conditions. This method applies kernel fuzzy c-means algorithm clustering heat rate data. Taking the mean error of 5-fold cross-validation as fitness value of parameters selection for LS-SVM,LS-SVM based on SFLA is trained and established local model for each cluster,and then the model output is obtained by the switch way,so as to realize the heat rate multi-model method. Compared with the single LS-SVM model and BP network heat rate prediction model,the multi-model has a higher prediction accuracy and better generalization ability.