摘要
针对神经形态芯片辐射源建模难题,提出了一种基于卷积神经网络(convolutional neural network,CNN)和全连接网络(fully connected network,FCN)的辐射源重构方法。该方法以传统等效源理论为基础,通过近场扫描点与偶极子位置的关系构建系数矩阵,并将其作为神经网络的输入,对应磁场幅度作为输出,利用神经网络建立偶极子与扫描场之间的映射关系。通过数值仿真和实验测量进行方法验证,结果表明,该方法通过将扫描区域外平面的系数矩阵输入训练好的神经网络可以准确预测对应的磁场幅度,其预测结果与仿真结果及测量结果的相对误差均在5%左右,具有良好的精度。
To address the challenges in radiation source modeling for neuromorphic chips,this paper proposes a radiation source reconstruction method based on convolutional neural network(CNN)and fully connected network(FCN).Building upon traditional equivalent source theory,the method constructs a coefficient matrix from the relationship between near-field scanning points and dipole positions,which serves as the input to the neural network,while the corresponding magnetic field amplitude is set as the output.This establishes a mapping between dipoles and scanned fields via the neural network.The proposed method is validated through numerical simulations and experimental measurements.Results demonstrate that by inputting the coefficient matrix of the out-of-plane scanning region into the trained neural network,the method can accurately predict the corresponding magnetic field amplitude.The predicted results exhibit a relative error of approximately 5%when compared to both simulation and measurement data,indicating good prediction accuracy.
作者
莫利楠
李燕
MO Linan;LI Yan(College of Information Engineering,China Jiliang University&Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province,Hangzhou 310018,China)
出处
《电波科学学报》
北大核心
2025年第3期503-510,共8页
Chinese Journal of Radio Science
基金
国家自然科学基金(62071424)
浙江省自然科学基金重大项目(LHZSD25F010001)。
