The Study Of RBF Networks And Genetic Algorithms And Their Application To Chemical Engineering  Posted on:20040625  Degree:Master  Type:Thesis  Country:China  Candidate:Q F Zheng  Full Text:PDF  GTID:2168360092981240  Subject:Chemical computer simulation and systems engineering  Abstract/Summary:  PDF Full Text Request  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 RBFPCR and RBFPLSR model is built. The PCR and PLSR picksup 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 RBFCSR model is produced. The model is applied to the actual chemical engineering process. Comparing with the RBFPCR and RBFPLSR, the RBFCSR uses its high generalization ability to solve the regressive problem, and finds the optimal coefficient in winder space. So the RBFCSR 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 CGARBFN 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.
 Keywords/Search Tags:  artifical neural networks, radial basis function networks, leastsquares regression, principal component regression, partial least squares regression, cyclic subspace regression, genetic algorithms, eugenic evolution strategy, chaos  PDF Full Text Request  Related items 
 
