电气工程及其自动化毕业设计（论文）外文翻译-遗传算法在非线性模型中的应用_精品.doc

郑州航空工业管理学院 英 文 翻 译 2011 届 电气工程及其自动化 专业 0706073 班级 题 目 遗传算法在非线性模型中的应用 姓 名 学号 指导教师 职称 副教授 二О一 一 年 三 月 三十 日 英语原文： Application of Genetic Programming to Nonlinear Modeling Introduction Identification of nonlinear models which are based in part at least on the underlying physics of the real system presents many problems since both the structure and parameters of the model may need to be determined. Many methods exist for the estimation of parameters from measures response data but structural identification is more difficult. Often a trial and error approach involving a combination of expert knowledge and experimental investigation is adopted to choose between a number of candidate models. Possible structures are deduced from engineering knowledge of the system and the parameters of these models are estimated from available experimental data. This procedure is time consuming and sub-optimal. Automation of this process would mean that a much larger range of potential model structure could be investigated more quickly. Genetic programming (GP) is an optimization method which can be used to optimize the nonlinear structure of a dynamic system by automatically selecting model structure elements from a database and combining them optimally to form a complete mathematical model. Genetic programming works by emulating natural evolution to generate a model structure that maximizes (or minimizes) some objective function involving an appropriate measure of the level of agreement between the model and system response. A population of model structures evolves through many generations towards a solution using certain evolutionary operators and a “survival-of-the-fittest” selection scheme. The parameters of these models may be estimated in a separate and more conventional phase of the complete identification process. Application Genetic programming is an established technique which has been applied to several nonl