CAJ | 학술논문

目前使用比较普遍的优化方法对BP算法改进之后，改进的BP神经网络预测过程都存在复杂程度变大、更加消耗人力资源等缺陷。针对这些缺陷，本文提出一种传递函数自我优化算法来改进神经网络。然后将改进的网络应用到风电功率预测中，以东北某风电场一段时间的风电运行数据作为实验样本，分别采用传统BP神经网络和改进的BP神经网络进行预测分析。仿真结果证明，改进之后的BP神经网络不仅有更快的收敛速度，还有更加精确的预测结果，并且不需要认为干预整个预测过程。极大提高了网络的预测能力和效率。
목전사용비교보편적우화방법대BP산법개진지후，개진적BP신경망락예측과정도존재복잡정도변대、경가소모인력자원등결함。침대저사결함，본문제출일충전체함수자아우화산법래개진신경망락。연후장개진적망락응용도풍전공솔예측중，이동북모풍전장일단시간적풍전운행수거작위실험양본，분별채용전통BP신경망락화개진적BP신경망락진행예측분석。방진결과증명，개진지후적BP신경망락불부유경쾌적수렴속도，환유경가정학적예측결과，병차불수요인위간예정개예측과정。겁대제고료망락적예측능력화효솔。
Using common optimization method to improve the BP algorithm will introduce shortcomings such as in-creased complexity and more human resource consumption in prediction process. To solve the problem, this paper presents a transfer function self-optimization algorithm for neural network improvement. The improved network is fur-ther applied to wind power prediction. Taking the operating data of a time period at a northeast wind farm as experi-mental samples, both traditional and the improved BP neural network are used to analyze the prediction. The results show that the improved BP neural network enhances not only convergence rate but also prediction accuracy.