Cost Estimating of Weapons Development Based on Rough Sets and ANN Learning
There are some difficulties in using Linearity Regression method to predict the cost of MLRS development under the small sample situation. On the basis of the capacity of dealing with the nonlinear of ANN and the learning capacity of Rough Sets (RS), a new cost estimating method combined with RS and neural network is brought forward, which can use the Relative Reduce theory in Rough Sets to learn or mine the knowledge concealed in the samples, then certain elements after reduce is selected as the inputs of neural network the cost estimating of weapons development is achieved. An example is provided to prove the precision of the new method is higher than that of the gray model.
Artificial neural network Rough set Gray theory Weapons system Cost estimating
Wu Xiao-yun Xing Li-xin Tao Hai-jun Lu Songsheng Chen Yun-fei
Army Officer Academy, Hefei, Anhui province, 230031, China
国际会议
三亚
英文
212-215
2012-01-06（万方平台首次上网日期，不代表论文的发表时间）