電氣工程及其自動化畢業(yè)設計英語翻譯--遺傳算法在非線性模型中的應用-文庫吧
2025-01-02 02:27 本頁面
【正文】 f agreement between the model and system responses. One examples is (1) Where is the error between model output and experimental data for each of N data points. The GP algorithm constructs and reconstructs model structures from the function library. Simplex and simulated annealing method and the fitness of that model is evaluated using a fitness function such as that in Eq.(1). The general fitness of the population improves until the GP eventually converges to a model description of the system.The Genetic programming algorithm For this research, a steadystate Geneticprogramming algorithm was used. At each generation, two parents are selected from the population and the offspring resulting from their crossover operation replace an existing member of the same population. The number of crossover operations is equal to the size of the population . the crossover rate is 100℅. The crossover algorithm used was a subtree crossover with a limit on the depth of the resulting tree. Genetic programming parameters such as mutation rate and population size varied according to the application. More difficult problems where the expected model structure is plex or where the data are noisy generally require larger population sizes. Mutation rate did not appear to have a significant effect for the systems investigated during this research. Typically, a value of about 2℅ was chosen. The function library varied according to application rate and what type of nonlinearity might be expected in the system being identified. A core of linear blocks was always available. It was found that specific nonlinearity such as lookup tables which represented a physical phenomenon would only be selected by the Genetic Programming algorithm if that nonlinearity actually existed in the dynamic system. This allows the system to be tested for specific nonlinearities.Programming model structure identification Each member of the Genetic Programming population represents a candidate model for the system. It is necessary to evaluate each model and assign to it some fitness value. Each candidate is integrated using a numerical integration routine to produce a time response. This simulation time response is pared with experimental data to give a fitness value for that model. A sum of squared error function (Eq.(1)) is used in all the work described in this paper, although many other fitness functions could be used. The simulation routine must be robust. Inevitably, some of the candidate mod
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