电光与控制, 2018, 25 (5): 64, 网络出版: 2021-01-20
Operational Effectiveness Assessment for Electronic Warfare UAVs Based on Improved WNN
Operational Effectiveness Assessment for Electronic Warfare UAVs Based on Improved WNN
电子战无人机 作战效能评估 遗传算法 小波神经网络 electronic warfare UVA opeartional effectiveness evaluation Genetic Algorithm (GA) Wavelet Neural Network (WNN)
摘要
小波神经网络(WNN)采用梯度下降法调整连接权值和伸缩平移尺度, 存在收敛速度慢, 易陷入局部极值等缺点。提出了基于遗传算法(GA)优化小波神经网络的电子战无人机作战效能评估模型。该评估模型在小波神经网络的基础上, 采用遗传算法搜索最优初始的小波神经网络连接权值和伸缩平移尺度, 避免了人为设定连接权值和伸缩平移尺度的盲目性。仿真实验结果表明此模型可以准确有效地对电子战无人机进行作战效能评估。
Abstract
Wavelet Neural Network (WNN) uses the gradient descent method to adjust the connecting weight and the scales for expanding/contracting and translating, which has the shortcomings of slow convergence speed and the local extremum. A method for assessing the operational effectiveness of electronic warfare UAVs based on Genetic Algorithm WNN (GA-WNN) is proposed. Based on WNN, the evaluation model uses GA to find the initial optimal WNN connecting weights, scaling parameters, and translating parameters. It avoids the blindness of artificial parameter setting. Simulation results show that this model can accurately and effectively assess the operational effectiveness of electronic warfare UAVs.
陈侠, 胡乃宽. Operational Effectiveness Assessment for Electronic Warfare UAVs Based on Improved WNN[J]. 电光与控制, 2018, 25(5): 64. CHEN Xia, HU Naikuan. Operational Effectiveness Assessment for Electronic Warfare UAVs Based on Improved WNN[J]. Electronics Optics & Control, 2018, 25(5): 64.
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