【作者】 郑启富；

【导师】 陈德钊；

【作者基本信息】 浙江大学 ， 化工计算机仿真与系统工程， 2003， 硕士

【摘要】 建立准确的模型对化工过程的理论研究和实际应用都具有重要的意义。但是，化工过程往往比较复杂，而且机理也不甚明了，很难直接通过机理建立准确的模型。神经元网络建模不需要考虑机理，根据样本数据即可建立起过程模型。本文重点研究了径向基函数网络(Radial Basis Function Networks，RBF网络)学习方法的改进和网络结构的优化；系统论述了遗传算法(Genetic Algorithms，GA)，并对它做了有效的改进，将遗传算法应用于参数的估计和优化，取得了较好的效果。全文主要内容如下： (1)将主成分回归(PCR)和偏最小二乘回归(PLSR)引入RBF网络中，建立了RBF—PCR模型和RBF—PLSR模型。由于PCR和PLSR从原有自变量数据矩阵中提取相互正交的成分，并略去方差非常小的成分，从而消除了原有自变量间所存在的复共线性，使回归过程更为稳健。 (2)将循环子空间回归(CSR)引入到RBF网络中，建立了RBF—CSR模型，并将它应用于实际化工过程。与RBF—PCR、RBF—PLSR相比，RBF—CSR运用了更为泛化的方法求解回归问题，在更广的范围内寻求最优的网络权值，因此，它所建立的模型是一种更优的网络回归模型。 (3)针对常规遗传算法(SGA)寻优效率偏低、易陷入局部最优的弱点，通过增加单纯形寻优算子、改进交叉算子、自适应地调整交叉率和变异率等措施，设计了一种优进遗传算法(Eugenic Evolution Genetic Algorithms，EGA)，并将它应用于动力学模型的参数估计，取得了良好的效果。 (4)利用混沌变量的遍历性，将其引入到遗传操作，设计了一种混沌遗传算法(Chaos Genetic Algorithms，CGA)。将CGA应用于RBF网络隐含层结构的优化，并设计了一种适应度函数，提出了CGA—RBFN模型。该模型应用于烃类热裂解丙烯产率的预测，取得了满意的效果。 文章最后对所做的工作进行了总结，并在此基础上，提出了今后的研究发展方向。更多还原

【Abstract】 Accurate models are important to the research and application of chemical engineering process. However, most problems in chemical engineering process are complex and we know little about their principles. So it is difficult to build accurate models directly by the principles. Neural networks build models without the principles, it modeling chemical engineering process by sample data. The main focus of this thesis is on improving the learning algorithms of the radial basis function networks (RBF networks), and optimizing the structure of the RBF networks, and reviewing the genetic algorithms (GA) and improving it effectively, and applying the GA to estimating and optimizing parameters and good effect is obtained. This article includes the following parts mainly:(1) The principal component regression (PCR) and partial least square regression (PLSR) methods are applied to determine the weight of the RBF networks, the RBF-PCR and RBF-PLSR model is built. The PCR and PLSR picks-up the orthogonal components form the primary independent variable data matrix, and ignores the components of very little variance. So they eliminate the multicollinearity between the primary independent variables and ensure the regression process being steady.(2) Through the cyclic subspace regression (CSR) being applied to determine the weight of the RBF networks, the RBF-CSR model is produced. The model is applied to the actual chemical engineering process. Comparing with the RBF-PCR and RBF-PLSR, the RBF-CSR uses its high generalization ability to solve the regressive problem, and finds the optimal coefficient in winder space. So the RBF-CSR model is a better networks regression model.(3) An eugenic evolution genetic algorithm (EGA) is proposed to improve the efficiency of simple genetic algorithm (SGA) searching and the performance of global optimization through introducing deterministic simplex searching operation, and improving the crossover operator, and modifying adaptive crossover probability andadaptive mutation probability, and others. The EGA is applied to estimate the kinetic model parameter and perfect results are obtained.(4) A chaos genetic algorithms (CGA) is designed through introducing chaos variable to genetic algorithm by the full use of its ergodic property. The CGA is applied to optimize the hidden layer structure of the RBF networks, at the same time a fitness function is designed, then the CGA-RBFN model is proposed. The model is applied to predict the propylene yield in the course of the thermal cracking of hydrocarbon, satisfied result is obtained.In the end of this paper, we make a summary and describe the further works.更多还原