CAJ | 학술논문

BP 神经网络是一种按误差逆传播算法训练的多层前馈网络,其与遗传算法的结合可以得到一种拥有良好的全局优化搜索和局部时频特性的学习训练途径.针对 BP 网络的不足,提出了一种基于改进遗传算法的 BP 神经网络控制器,此方法可以克服基本遗传算法收敛速度慢,容易陷入“早熟”收敛,计算稳定性不好等一系列问题,进一步提高了 BP 神经网络控制器的性能.最后通过对轧制力模型的预报仿真,证明了控制器的有效性.
BP 신경망락시일충안오차역전파산법훈련적다층전궤망락,기여유전산법적결합가이득도일충옹유량호적전국우화수색화국부시빈특성적학습훈련도경.침대 BP 망락적불족,제출료일충기우개진유전산법적 BP 신경망락공제기,차방법가이극복기본유전산법수렴속도만,용역함입“조숙”수렴,계산은정성불호등일계렬문제,진일보제고료 BP 신경망락공제기적성능.최후통과대알제력모형적예보방진,증명료공제기적유효성.
@@@@BP neural network is a kind of error back propagation training algorithm for the multilayer feed forward network. The combination of genetic algorithm and BP neural network can get one type of training way with good global optimization search and local time-frequency characteristics. This paper aims at the shortage of BP network, and proposes a kind of BP neural network controller based on improved genetic algorithm, which can overcome a series of problems, such as the slow convergence of standard genetic algorithm, falling into the premature convergence easily, and the instability of computation. It further improves the performance of BP neural network controller. Finally, through the simulation of the rolling force prediction model, the paper proves the effectiveness of the proposed controller.