摘要
为了解决甘蔗收获机械剥叶性能评价中权重确定的关键问题,构建了三层前馈BP神经网络,并采用正交试验数据构造训练样本,以提高训练速度及精度.在此基础上通过运用经训练后的BP神经网络的各连接权值,确定了反映各目标因素对评价指标影响程度的权重值.运用BP神经网络方法可确保经确定的权重能如实地反映出各目标因素对评价指标的重要程度.
A three-layer-feedforward BP neural network is investigated in order to solve the key problem of the determination of weights in the evaluation performance of the cleaning performance of the sugarcane harvester. The training samples of the BP neural network are made up of the orthogonal experimental data to enhance the training speed and precision. And then the connecting weights of the trained BP neural network are used to compute the weights of the target factors on the evaluation indexes. The results show that the weights determined by the BP neural network can truthfully reflex the importance of the target factors on the evaluation indexes.
出处
《广西大学学报（自然科学版）》
CAS
CSCD
2005年第3期233-237,共5页
Journal of Guangxi University（Natural Science Edition）
基金
国家自然科学基金(No.50365001)
关键词
甘蔗收获机械
剥叶性能
BP神经网络
权重
正交试验
sugarcane harvester
cleaning performance
BP neural network
weight
orthogonal experiment
