摘要
运用粗糙集方法和信息熵概念,在不改变训练样本分类质量的条件下,按照输入影响因素相对于输出的重要度的大小,对输入参数集进行约简,确定神经网络输入层变量和神经元个数。通过对典型样本的学习,建立粗糙集BP神经网络多因素预测模型,将其用于导弹系统研制费用预测。结果表明,该方法减少了网络的训练时间,改善了学习效率,具有较高的预测精度,是可行的、有效的。
In term of the important degree of input influence factor to output, rough set approach and the conception of information entropy are employed to reduce the parameters of the input parameter set with no changing classification quality of samples. Thus, the number of the input variables and neurons is gotten, and the multi-factor estimation model based on rough set and BP artificial network is set by learning from the typical samples. Its application to the cost estimation of missile system is given. It is shown that the approach can reduce the training time, improve the learning efficiency, enhance the predication accuracy, and be feasible and effective.
出处
《计算机工程》
CAS
CSCD
北大核心
2007年第5期154-155,共2页
Computer Engineering
基金
空军工程大学博士论文创新基金资助项目
关键词
粗糙集
神经网络
信息熵
多因素预测
费用预测
Rough set
Neural network
Information entropy
Multi-factor estimation
Cost estimation
