计算机与应用化学
計算機與應用化學
계산궤여응용화학
COMPUTERS AND APPLIED CHEMISTRY
2013年
11期
1329-1332
,共4页
最小二乘支持向量机%散乱数据拟合%参数化优化
最小二乘支持嚮量機%散亂數據擬閤%參數化優化
최소이승지지향량궤%산란수거의합%삼수화우화
Least squares support vector regression%scatter data fitting%parametric optimization
针对参数化优化问题需要反复求解以适应模型参数变化，从而累积大量经验数据的特点，提出可根据经验数据估计新参数所对应最优解的思想。由于所考虑的优化问题往往涉及多个参数的扰动或变化，最优解的估计问题可以考虑为多元散乱数据拟合问题，本文采用最小二乘支持向量回归(LS-SVR)方法实现了该问题的一般解法。选用无需额外设置参数的无穷结点线性样条核，并提出了集成两层优化框架的改进LS-SVR方法，完全避免了人为整定算法调节参数带来的误差。通过参数化蒸馏塔优化模型的数值实验，对比了改进的LS-SVR与最近邻插值方法、径向基网络的估计效果，结果表明，改进的LS-SVR方法所估计的结果最贴近真实值。改进的LS-SVR方法用优化算法替代人为的参数整定，能够高效准确地估计参数化优化问题的最优解，从而明显提高优化的求解效率。
針對參數化優化問題需要反複求解以適應模型參數變化，從而纍積大量經驗數據的特點，提齣可根據經驗數據估計新參數所對應最優解的思想。由于所攷慮的優化問題往往涉及多箇參數的擾動或變化，最優解的估計問題可以攷慮為多元散亂數據擬閤問題，本文採用最小二乘支持嚮量迴歸(LS-SVR)方法實現瞭該問題的一般解法。選用無需額外設置參數的無窮結點線性樣條覈，併提齣瞭集成兩層優化框架的改進LS-SVR方法，完全避免瞭人為整定算法調節參數帶來的誤差。通過參數化蒸餾塔優化模型的數值實驗，對比瞭改進的LS-SVR與最近鄰插值方法、徑嚮基網絡的估計效果，結果錶明，改進的LS-SVR方法所估計的結果最貼近真實值。改進的LS-SVR方法用優化算法替代人為的參數整定，能夠高效準確地估計參數化優化問題的最優解，從而明顯提高優化的求解效率。
침대삼수화우화문제수요반복구해이괄응모형삼수변화，종이루적대량경험수거적특점，제출가근거경험수거고계신삼수소대응최우해적사상。유우소고필적우화문제왕왕섭급다개삼수적우동혹변화，최우해적고계문제가이고필위다원산란수거의합문제，본문채용최소이승지지향량회귀(LS-SVR)방법실현료해문제적일반해법。선용무수액외설치삼수적무궁결점선성양조핵，병제출료집성량층우화광가적개진LS-SVR방법，완전피면료인위정정산법조절삼수대래적오차。통과삼수화증류탑우화모형적수치실험，대비료개진적LS-SVR여최근린삽치방법、경향기망락적고계효과，결과표명，개진적LS-SVR방법소고계적결과최첩근진실치。개진적LS-SVR방법용우화산법체대인위적삼수정정，능구고효준학지고계삼수화우화문제적최우해，종이명현제고우화적구해효솔。
Parametric optimization problems require repeated solving to adapt changes of the model parameters, and a large amount of empirical data can be accumulated. A new approach is proposed in this paper, which estimates the optimal solution according to the new parameters based on the empirical data. Since the considered optimization problems often involve multiple-parameter disturbance or change, the problem of estimating the optimal solution is considered as a multivariate scattered data fitting problem. The least squares support vector regression (LS-SVR) method is applied to achieve the solution. The artificial tuning of the algorithm option parameters are completely avoided because the infinite-node linear spline kernel without setting parameters is chosen and an integrated two-layer optimization framework is proposed. According to the numerical experiments about the parametric optimization model of a distillation column, the estimation efficiency of the improved LS-SVR, the nearest neighbor interpolation method and RBF network estimation is compared. The numerical results illustrate that the estimated values from the improved LS-SVR method are most close to the real ones. The improved LS-SVR method adopts optimization algorithms to replace artificial parameter tuning, which results in high efficiency of the optimal estimation in parametric optimization problems.
