外文翻译---遗传算法在非线性模型中的应用(编辑修改稿)

外文翻译---遗传算法在非线性模型中的应用(编辑修改稿)内容摘要：

e robust. Inevitably, some of the candidate models will be unstable and therefore, the simulation program must protect against overflow error. Also, all system must return a fitness value if the GP algorithm is to work properly even if those systems are unstable. Parameter estimation Many of the nodes of the GP trees contain numerical parameters. These could be the coefficients of the transfer functions, a gain value or in the case of a time delay, the delay itself. It is necessary to identify the numerical parameters of each nonlinear model before evaluating its fitness. The models are randomly generated and can therefore contain linearly dependent parameters and parameters which have no effect on the output. Because of this, gradient based methods cannot be used. Geic Programming can be used to identify numerical parameters but it is less efficient than other methods. The approach chosen involves a bination of the NelderSimplex and simulated annealing methods. Simulated annealing optimizes by a method which is analogous to the cooling process of a metal. As a metal cools, the atoms organize themselves into an ordered minimum energy structure. The amount of vibration or movement in the atoms is dependent on temperature. As the temperature decreases, the movement, though still random, bee smaller in amplitude and as long as the temperature decreases slowly enough, the atoms order themselves slowly enough, the atoms order themselves into the minimum energy structure. In simulated annealing, the parameters start off at some random value and they are allowed to change their values within the search space by an amount related to a quantity defined as system „temperature‟. If a parameter change improves overall fitness, it is accepted, if it reduces fitness it is accepted with a certain probability. The temperature decreases according to some predetermined „cooling‟ schedule and the parameter values should converge to some solution as the temperature drops. Simulated annealing has proved particularly effective when bines with other numerical optimization techniques. One such bination is simulated annealing with Neldersimplex is an (n+1) dimensional shape where n is the number of parameters. This simples explores the search space slowly by changing its shape around the optimum solution .The simulated annealing adds a random ponent and the temperature scheduling to the simplex algorithm thus improving the robustness of the method . This has been found to be a robust and reasonably efficient numerical optimization algorithm. The parameter estimation phase can also be used to identify other numerical parameters in part of the model。