基于GABP神經(jīng)網(wǎng)絡(luò )曲線(xiàn)擬合的快沿電磁脈沖信號源模型求解

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基于GABP神經(jīng)網(wǎng)絡(luò )曲線(xiàn)擬合的快沿電磁脈沖信號源模型求解
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作者單位:(1.軍械工程學(xué)院 靜電與電磁防護研究所,石家莊 050003;2.總裝備部 工程兵軍事代表局駐武漢軍事代表室,武漢 430073;3.中國解放軍63981部隊,武漢 430311)
作者簡(jiǎn)介:紀志強(1989),男,福建泉州人,碩士研究生,主要從事電磁仿真與效應評估技術(shù)方向的研究。[FQ)]
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基金項目:國家自然科學(xué)基金(51277181) 。 

Modeling of FREMP Source Based on GABP Neural Network

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(1.Research Institute of Static Electricity & Electromagnetic Protection, Ordnance Engineering College, Shijiazhuang 050003, China;2.Wuhan Military Representative Office of the General Armament Engineering Department, Wuhan 430073, China; ;3.Unit 63981, Wuhan 430311, China)

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摘要:

為給電子設備的電磁脈沖效應仿真提供準確的快沿電磁脈沖(fast rise-time electromagnetic pulse,FREMP)信號源模型,提出一種基于遺傳算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò )(GABP-NN)曲線(xiàn)擬合的信號源模型求解方法;該方法通過(guò)示波器對脈沖信號進(jìn)行采集,利用GABP神經(jīng)網(wǎng)絡(luò )對波形曲線(xiàn)進(jìn)行高精度擬合,提取網(wǎng)絡(luò )參數建立信號源模型;為進(jìn)一步獲得BP神經(jīng)網(wǎng)絡(luò )擬合規律設置對比實(shí)驗,采用隱含層神經(jīng)元數為10的GABP神經(jīng)網(wǎng)絡(luò )對FREMP信號源進(jìn)行建模,所得模型擬合度為91.64%;仿真結果表明該方法運算速度快、精度高。

Abstract:

In order to provide an accurate FREMP (fast rise-time electromagnetic pulse) source model for electronic devices EMP effect simulations, a curve fitting and modeling method based on GABP-NN (BP neural network optimized by genetic algorithms) is proposed. In this method, the FREMP signal collected by oscilloscope is used for high precision waveform curve fitting by GABP-NN and the network parameters is extracted to establish the source modeling. Comparative experiments are set for finding the fitting rule of BP neural network. Using GABP-NN for FREMP source modeling with 10 hidden layer neurons, the fit was 91.64%. The simulation results show that this method does modeling well and has a high computation speed.

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紀志強,魏明,吳啟蒙,樊高輝,魏晗.基于GABP神經(jīng)網(wǎng)絡(luò )曲線(xiàn)擬合的快沿電磁脈沖信號源模型求解計算機測量與控制[J].,2015,23(5):1792-1794, 1801.

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在線(xiàn)發(fā)布日期: 2015-07-31
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