RBF-CSR-EHCGA模型及其在脉冲萃取中的应用  

RBF-CSR-EHCGA model and its application to pulsed extraction

作　　者：成飙[1] 陈德钊[1] 吴晓华[1] 

机构地区：[1]浙江大学化学工程与生物工程学系,浙江杭州310027

出　　处：《化工学报》2005年第7期1271-1275,共5页CIESC Journal

基　　金：国家自然科学基金项目(20276063).~~

摘　　要：径向基函数循环子空间回归(RBFCSR)是一种有效的非线性网络模型,以高斯条为基函数,性能更优,但其参数多,且难以选定,将显著影响模型性能.为此,本文提出基于优进策略的混合编码遗传算法(EHCGA),以不同的方式为各类参数编码,并引入确定性的Powell算子,提高全局搜优效率.EHCGA算法以模型预报性能为目标,优选参数,以此建立RBFCSREHCGA模型,它的预报精度高、稳定性良好.已成功应用于回收己内酰胺的脉冲萃取过程建模,效果良好,明显优于其他网络模型,也优于近似机理模型.Radial basis function-cyclic subspace regression （RBF-CSR） approach is a rapid one-step modeling method, escaping the difficulty of ANN architecture design. ANN and multivariate analysis are integrated into this approach, but it is difficult to optimize the parameters of the model because the method is a mixed integer programming. A eugenic hybrid coding genetic algorithm （EHCGA）, which used different coding methods for different type of variables was proposed to give the optimal parameters of the RBF-CSR model. In EHCGA deterministic optimization method was added to standard GA based on eugenic strategy, which could greatly improve the convergence speed and accuracy of GA. RBF-CSR model optimized by EHCGA called RBF-CSR-EHCGA model was successfully applied to modeling recovery of caprolactam from waste water by using pulsed-sieve-plate extraction column.

关 键 词：径向基函数 循环子空间回归 遗传算法 混合规划 脉冲萃取 参数优化 

分 类 号：TQ028.96[化学工程]