改进BP神经网络的武警装甲车保障能力评估

doi:10.3969/j.issn.0255-8297.2016.04.011

Abstract

Abstract:

There are many factors affecting security of Chinese armed police force (CAPF) armored vehicles in wartime and peacetime. To overcome ambiguity and uncertainty in the evaluation of security capability of CAPF armored vehicles, this paper establishes an evaluation index system using glowworm swarm to optimize a BP neural network. Having determined the initial weights and thresholds, a security of CAPF armored vehicles is evaluated. By establishing a model, calculation and analysis are performed. It is concluded that the glowworm swarm optimization BP (GSOBP) neural network converges fast and is accurate. The method can be used effectively for evaluating security of the CAPF wheeled armored anti-riot vehicles.

Key words: support ability, evaluation, glowworm swarm optimization algorithm, BP neural network

CLC Number:

TP391.9

SHAN Ning, BAN Chao, DENG Chun-ze. Evaluation of Support Capability of CAPF ArmouredVehicle with Improved BP Neural Network[J]. Journal of Applied Sciences, 2016, 34(4): 461-468.

References

[1] 白炜. 基于神经网络的作战效能评估方法研究[D]. 长沙:国防科学技术大学, 2007.
[2] 王正元, 刘靖旭, 谭跃进, 沙红兵. 基于作战仿真的装甲车辆作战效能评估方法[J]. 国防科技大学学报, 2004, 26(2): 106-109. Wang Z Y, Liu J X, Tan Y J, Sha H B. Combat effectiveness evaluation of the armored vehicle based on combat simulation [J]. Journal of National University of Defense Technology, 2004, 26(2): 106-109. (in Chinese)
[3] 刘芬良, 罗权, 李泽恩. 某型两栖装甲车作战效能评估模型[J]. 火力与指挥控制, 2014, 39(2): 169-172. Liu F L, Luo Q, Li Z E. Model of operational effectiveness evaluation in amphibious armoured vehicle [J]. Fire Control & command Control, 2014, 39(2): 169-172. (in Chinese)
[4] Jin W, Li Z J, Wei L S. The improvements of BP neural network learning algorithm [C]//Signal Processing Proceedings. WCCC-ICSP 2000. 5th International Conference on IEEE, 2000, 3: 1647-1649.
[5] Xiao Z, Ye S J, Zhong B. BP neural network with rough set for short term load forecasting[J]. Expert Systems with Applications, 2009, 36(1): 273-279.
[6] 李智舜, 吴明曦. 军事装备保障学[M]. 北京:军事科学出版社, 2009.
[7] 周伟祝, 宦婧, 孙媛, 王永安. 一种基于神经网络的装备保障方案评估模型[J]. 计算机仿真, 2013, 30(2): 303-307. Zhou W Z, Huan J, Sun Y, Wang Y A. Evaluation model of material support plan based on neural network [J]. Computer Simulation, 2013, 30(2): 303-307. (in Chinese)
[8] 刘长平, 叶春明. 一种新颖的仿生群智能优化算法: 萤火虫算法[J]. 计算机应用研究, 2011, 28(9): 3295-3297. Liu C P, Ye C M. Novel bioinspired swarm intelligence optimization algorithm: firefly algorithm[J]. Application Research of Computers, 2011, 28(9): 3295-3297. (in Chinese)
[9] 朱文超, 许德章. 一种基于人工萤火虫群优化的改进粒子滤波算法[J]. 计算机应用研究, 2014, 31(10): 2920-2924. Zhu W C, Xu D Z. Improved particle filter algorithm based on artificial glowworm swarm optimization [J]. Application Research of Computers, 2014, 31(10): 2920-2924. (in Chinese)
[10] 符强, 童楠, 赵一鸣. 一种基于多种群学习机制的萤火虫优化算法[J]. 计算机应用研究, 2013, 30(12): 3600-3602. Fu Q, Tong N, Zhao Y M. Firefly algorithm based on multi-group learning mechanism [J]. Application Research of Computers, 2013, 30(12): 3600-3602. (in Chinese)
[11] 王改革, 郭立红, 段红, 刘逻, 王鹤淇. 基于萤火虫算法优化BP 神经网络的目标威胁估计[J]. 吉林大学学报:工学版, 2013, 43(4): 1064-1069. Wang G G, Guo L H, Duan H, Liu L, Wang H Q. Target threat assessment using glowworm swarm optimization and BP neural network [J]. Journal of Jilin University:Engineering and Technology Edition, 2013, 43(4): 1064-1069. (in Chinese)
[12] 候越, 赵贺, 路小娟. 基于萤火虫优化的BP神经网络算法研究[J]. 兰州交通大学学报, 2013, 32(6): 24-27. Hou Y, Zhao H, Lu X J. Study on glowworm swarm optimized BP neural network algorithm[J]. Journal of Lanzhou Jiaotong University, 2013, 32(6): 24-27. (in Chinese)
[13] Krishnanand K N, Ghose D. Glowworm swarm optimisation: a new method for optimising multi-modal functions [J]. International Journal of Computational Intelligence Studies, 2009, 1(1): 93-119.
[14] Zhou Y, Zhou G, Wang Y. A glowworm swarm optimization algorithm based tribes [J]. Applied Mathematical Modelling, 2013, 7(2L): 537-541.
[15] Krishnanand K N, Ghose D. Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions [J]. Swarm Intelligence, 2009, 3(2): 87-124.
[16] Wu B, Qian C, Ni W. The improvement of glowworm swarm optimization for continuous optimization problems [J]. Expert Systems with Applications, 2012, 39(7): 6335-6342.
[17] 飞思科技产品研发中心. 神经网络理论与Matlab7 实现[M]. 北京:电子工业出版社, 2005.

Evaluation of Support Capability of CAPF ArmouredVehicle with Improved BP Neural Network

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