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论文:2016,Vol:34,Issue(1):25-32 |
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引用本文： |
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刘海波, 王和平, 沈立顶. 基于SAPSO优化灰色神经网络的空中目标威胁估计[J]. 西北工业大学学报 |
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Liu Haibo, Wang Heping, Shen Liding. Target Threat Assessment Using SAPSO and Grey Neural Network[J]. Northwestern polytechnical university |
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| 基于SAPSO优化灰色神经网络的空中目标威胁估计 |
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| 刘海波1, 王和平1,2, 沈立顶1 |
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1. 西北工业大学 航空学院, 陕西 西安 710072;
2. 西北工业大学 深圳研究院, 广东 深圳 518057 |
| 摘要: |
| 针对目标威胁估计有很多不确定性的特点,分析了传统目标威胁估计方法和灰色神经网络初始参数随机选择的不足。采用模拟退火改进的粒子群算法代替梯度修正法,对网络参数初始值进行寻优,并通过该方法搜寻到的最优粒子,建立了基于模拟退火粒子群算法优化的灰色神经网络模型,以提高预测模型的稳健性和精确度。与灰色神经网络和没有改进的粒子群灰色神经网络等方法进行比较,仿真实验结果表明,模拟退火粒子群优化的灰色神经网络具有很好的预测能力,可以准确地完成空中目标威胁估计。 |
| 关键词:
灰色系统
神经网络
模拟退火
粒子群算法
目标威胁估计
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| Target Threat Assessment Using SAPSO and Grey Neural Network |
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| Liu Haibo1, Wang Heping1,2, Shen Liding1 |
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1. College of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China;
2. Shenzhen Research Institute of Northwestern Polytechnical University, Shenzhen 518057, China |
| Abstract: |
| According to the characteristics of target threat estimation has a lot of uncertainty, Analyzed the defects of traditional target threat estimation method and gray neural network (GNNM) initial parameters randomly selected. Using simulated annealing (SA) improved particle swarm optimization (PSO) algorithm to search for the optimal values of initial parameters of network, The establishment of a simulated annealing particle swarm algorithm to optimize the gray neural network model, Improved the robustness and accuracy of prediction model. Comparing with GNNM and PSO-GNNM, The simulation results show that SAPSO-GNNM has good prediction ability, Have the ability to accurately estimate air targets threat Can be accurately estimated air targets threat. |
| Key words:
backpropagation algorithms
computer simulation
differential equations
eigenvalues and eigenfunctions
errors
estimation
flowcharting
forecasting
mathematical models
membership functions
neural networks
optimization
particle swarm optimization (PSO)
probability
regression analysis
simulated annealing
temperature
time series
topology
GNNM(Grey Neural Networks Model)
grey system
SAPSO(simulated annealing particle swarm optimization)
Target threat optimization.
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| 收稿日期: 2015-04-25
修回日期:
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| DOI: |
| 基金项目: 深圳市科技研发基金(CXZZ20120831170042239)资助 |
| 通讯作者:
Email： |
| 作者简介: 刘海波(1981-),西北工业大学博士研究生,主要从事智能算法与飞行器控制系统研究。
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