The Study Of RBF Networks And Genetic Algorithms And Their Application To Chemical Engineering

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.