電氣工程及其自動化畢業(yè)設(shè)計英語翻譯--遺傳算法在非線性模型中的應(yīng)用(已修改)
2025-01-29 02:27 本頁面
【正文】 英語原文：Application of Genetic Programming to Nonlinear ModelingIntroductionIdentification 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 bination 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 suboptimal. 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 bining them optimally to form a plete 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 “survivalofthefittest” selection scheme. The parameters of these models may be estimated in a separate and more conventional phase of the plete identification process.ApplicationGenetic programming is an established technique which has been applied to several nonlinear modeling tasks including the development of signal processing algorithms and the identification of chemical processes. In the identification of continuous time system models, the application of a block diagram oriented simulation approach to GP optimization is discussed by Marenbach, Bettenhausen and Gray, and the issues involved in the application of GP to nonlinear system identification are discussed in Gray’s another paper. In this paper, Genetic programming is applied to the identification of model structures from experimental data. The systems under investigation are to be represented as nonlinear time domain continuous dynamic models.The model structure evolves as the GP algorithm minimizes some objective function involving an appropriate measure of the level o
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