基于GA-SLFRWNN的空中目标威胁评估
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摘要
针对空战中目标威胁评估系统非线性、评估难度大且富含不确定信息的问题,研究了基于遗传算法优化模糊递归小波神经网络(single-hidden-layer fuzzy recurrent wavelet neural network optimized by genetic algorithm,GA-SLFRWNN)的目标威胁评估方法。首先通过分析威胁评估的影响因素及其信息的模糊性,将RWNN嵌入FNN的后件部分,以实现增强自学习能力的目的,然后采用GA对模型初始参数进行优化选取,并提出了基于李雅普诺夫理论的最优学习率。仿真实验表明:相比于FNN和FRWNN,该算法提高了系统的稳定性,加快了收敛速度,增强了预测精度。
In target threat assessment of air combat, the evaluation system model is usually nonlinear and the assessment which is difficult to obtain also has some uncertain information. In order to effectively solve these problems, the Single-hidden-layer Fuzzy Recurrent Wavelet Neural Network Optimized by Genetic Algorithm(GA-SLFRWNN) is presented in this paper. In this new method, the influence factors for assessment and the ambiguity of their information are first analyzed. The RWNN are embed in the back part of FNN(fuzzy neural network) for the purpose of enhancing self-learning ability. Then GA is used to optimize the initial parameters of the model and the optimal learning rate based on Lyapunov theory is proposed. The simulation results show that the proposed algorithm improves the stability of the evaluation system, accelerates the convergence speed and enhances the prediction accuracy compared with the FNN and SLFRWNN.
In target threat assessment of air combat, the evaluation system model is usually nonlinear and the assessment which is difficult to obtain also has some uncertain information. In order to effectively solve these problems, the Single-hidden-layer Fuzzy Recurrent Wavelet Neural Network Optimized by Genetic Algorithm(GA-SLFRWNN) is presented in this paper. In this new method, the influence factors for assessment and the ambiguity of their information are first analyzed. The RWNN are embed in the back part of FNN(fuzzy neural network) for the purpose of enhancing self-learning ability. Then GA is used to optimize the initial parameters of the model and the optimal learning rate based on Lyapunov theory is proposed. The simulation results show that the proposed algorithm improves the stability of the evaluation system, accelerates the convergence speed and enhances the prediction accuracy compared with the FNN and SLFRWNN.
引文
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[2] 李闯,端木京顺,雷英杰,等.基于认知图和直觉模糊推理的态势评估方法[J].系统工程与电子技术,2012,34(10):2064-2068LI Chuang,DUANMU Jingshun,LEI Yingjie,et al.Situation Assessment Based on Cognitive Maps and Intuitionistic Fuzzy Reasoning[J].Systems Engineering and Electronics,2012,34(10):2064-2068 (in Chinese)
[3] XU Y,MIU X.Multi-Attribute Decision Making Method for Air Target Threat Evaluation Based on Intuitionistic Fuzzy Sets[J].Journal of Systems Engineering and Electronics,2012,23(6):891-897
[4] 夏博龄,贺正洪,雷英杰.基于直觉模糊推理的威胁评估改进算法[J].计算机工程,2009,35(16):195-197XIA Boling,HE Zhenghong,LEI Yingjie.Improved Algorithm of Threat Assessment Based on Intuitionistic Fuzzy Reasoning[J].Computer Engineering,2009,35(16):195-197 (in Chinese)
[5] WANG Y,SUN Y,LI J Y,et al.Air Defense Threat Assessment Based on Dynamic Bayesia Network[C]//International Conference on Systems and Informatics,Yantai,2012:721-724
[6] 刘跃峰,陈哨东,赵振宇,等.基于FBNs的有人机/UCAV编队对地攻击威胁评估[J].系统工程与电子技术,2012,34(8):1635-1639LIU Yuefeng,CHEN Shaodong,ZHAO Zhenyu,et al.Threat Assessment of Manned/Unmanned Combat Aerial Vehicle Formation Air-to-Ground Attack Based on FBNs[J].Systems Engineering and Electronics,2012,34(8):1635-1639 (in Chinese)
[7] 曾守桢,穆志民.基于Zhenyuan积分的直觉模糊多属性决策方法[J].控制与工程,2018,33(3):542-548ZENG Shouzhen,MU Zhimin.Method Based on Zhenyuan Integral for Intuitionistic Fuzzy Multiple Attribute Decision Making[J].Control and Decision,2018,33(3):542-548 (in Chinese)
[8] 王宝成,栗飞,陈正.基于模糊TOPSIS法的空袭目标威胁评估[J].海军航空工程学院学报,2012,27(3):323-326WANG Baocheng,LI Fei,CHEN Zheng.Air-Attack Targets Threat Assessment Based on Fuzzy TOPSIS[J].Journal of Naval Aeronautical and Astronautical University,2012,27(3):323-326 (in Chinese)
[9] 王晓帆,王宝树.基于直觉模糊与计划识别的威胁评估方法[J].计算机科学,2010,37(5):175-177WANG Xiaofan,WANG Baoshu.Techniques for Threat Assessment Based on Intuitionistic Fuzzy Theory and Plan Recognition[J].Computer Science,2010,37(5):175-177 (in Chinese)
[10] 王改革.基于智能算法的目标威胁估计[D].长春:中国科学院长春光学精密机械与物理研究所,2013:67-71WANG Gaige.Target Threat Assessment Using Intelligence Algorithms[D].Changchun:Changchun Institute of Optics,Fine Mechanics and Physics Chinese Academy of Sciences,2013:67-71 (in Chinese)
[11] 罗艳春,郭立红,姜晓莲,等.基于模糊神经网络的空中目标威胁评估[J].微计算机信息,2007,23(34):268-270LUO Yanchun,GUO Lihong,JIANG Xiaolian,et al.Threat Assessment for Aerial Target Based on Fuzzy Neural Network[J].Microcomputer Information,2007,23(34):268-270 (in Chinese)
[12] LAM H K,LAUBER J.Membership-Function-Dependent Stability Analysis of Fuzzy-Model-Based Control Systems Using Fuzzy Lyapunov Functions[J].Informantion Science,2013,232(20):253-266
[13] 刘海波,王和平,沈立顶,等.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,2016,34(1):25-32LIU Haibo,WANG Heping,SHEN Liding,et al.Target Threat Assessment Using SAPSO and Grey Neural Network[J].Journal of Northwestern Polytechnical University,2016,34(1):25-32 (in Chinese)