CAJ | 학술논문

利用遗传算法所具有的自学习能力和非线性特性,解决励磁系统中非线性环节的辨识问题.通过建立待辨识励磁系统的模型,以励磁系统的实际输入作为模型的输入,利用遗传算法对模型参数进行优化,最终得到满足励磁系统建模要求的参数.用MATALAB下的仿真结果和现场试验结果进行对比,表明该算法能够较准确地辨识出励磁系统各个环节的参数.
이용유전산법소구유적자학습능력화비선성특성,해결려자계통중비선성배절적변식문제.통과건립대변식려자계통적모형,이려자계통적실제수입작위모형적수입,이용유전산법대모형삼수진행우화,최종득도만족려자계통건모요구적삼수.용MATALAB하적방진결과화현장시험결과진행대비,표명해산법능구교준학지변식출려자계통각개배절적삼수.
The learning capability and non-liner feature of genetic algorithm can be used to obtain the identification of non-liners in excitation system. By building model with excitation system and using the real input data as the input of model, the parameters satisfying modeling excitation system request can be obtained by adjusting the model parameters with generic algorithm. The identification results of a simulation system in MATLAB and a real excitation system show that the proposed method is able to obtain the accurate parameters of the excitation system.