| 基于Rough集理论和神经网络的武器系统参数费用模型 |
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| 引用本文： | 高尚. 基于Rough集理论和神经网络的武器系统参数费用模型[J]. 系统工程理论与实践, 2003, 23(4): 52-55. DOI: 10.12011/1000-6788(2003)4-52 |
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| 作者姓名： | 高尚 |
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| 作者单位： | 华东船舶工业学院电子与信息系 |
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| 摘 要： | 建立武器参数费用模型 ,首先要挑选特征参数 ,这里采用知识约简方法选择武器的特征参数 ;利用神经网络理论建立了参数费用模型 ,武器系统的费用与武器特征参数的关系可通过神经网络的阈值和权值得到体现 .通过实例对神经网络法与线性回归法所得的结果进行了比较 ,结果表明 ,神经网络法比线性回归法精确.
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| 关 键 词： | 参数费用模型 Rough集 知识约简 神经网络 B-P算法 |
| 文章编号： | 1000-6788(2003)04-0052-04 |
| 修稿时间： | 2002-01-21 |
Weapon System''s Parameter-cost Model Based on Rough Set Theory and Neural Network |
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| GAO Shang. Weapon System''s Parameter-cost Model Based on Rough Set Theory and Neural Network[J]. Systems Engineering —Theory & Practice, 2003, 23(4): 52-55. DOI: 10.12011/1000-6788(2003)4-52 |
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| Authors: | GAO Shang |
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| Affiliation: | Department of Electronics and Information, East China Shipbuilding Institute |
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| Abstract: | The character parameters of weapon system are selected based on reduction of knowledge. A parameter cost model is established by using neural network theory. The relationship among cost with characteristic parameters is described through threshold and weight of neural networks. The method is illustrated through examples. The results obtained from neural network method are compared with that from linear regression method. The comparing results show that the neural network method is more accurate than the linear regression method. |
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| Keywords: | parameter cost model rough set reduction of knowledge neural network B-P algorithm |
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