Hardness Prediction of Ferromagnetic Materials Based on Improved Random Forest Algorithm [基于改进随机森林算法的铁磁材料硬度预测] - Details

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Abstract：

To　improve　the　precision　of　the　ferromagnetic　material　hardness　prediction　method　based　on　Barkhausen　noise(BN)　and　automate　the　method,a　group　of　features　based　on　the　first　derivative　and　two　derivatives　of　the　auto　regression　(AR)　spectrum　of　BN　were　proposed.　A　feature　extraction　algorithm　was　designed　to　unify　the　dimension　of　the　frequency　domain　characteristics,and　the　group　voting　principle　of　random　forest　algorithm　was　improved　to　reduce　noise　interference　and　computational　complexity.　Through　the　hardness　prediction　experiments　of　two　metals,　the　expected　results　were　obtained.　Compared　with　the　mean　square　error　of　the　classical　time　domain　prediction　method　(229.8　and　298.7,　respectively),　the　mean　square　error　of　the　prediction　method　by　using　the　feature　and　algorithm　is　only　60.3　and　81.3,　respectively.　The　proposed　feature　and　method　have　obvious　accuracy　and　superiority.　©　2019,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.

Keyword：

Auto regression (AR) spectrum; Barkhausen noise (BN); CART algorithm; Ffeature extraction algorithm; Random forest; Second derivative

Author Community：

[ 1 ] [Sun, G.]College of Information and Communication Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 2 ] [Liu, H.]College of Information and Communication Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 3 ] [He, C.]College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, 100124, China
[ 4 ] [Li, Y.]College of Information and Communication Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 5 ] [Li, Z.]College of Information and Communication Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 6 ] [Liu, X.]College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, 100124, China
[ 7 ] [Zhang, R.]College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, 100124, China
[ 8 ] [Lu, H.]College of Information and Communication Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

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Source ：

Journal of Beijing University of Technology

ISSN： 0254-0037

Year： 2019

Issue： 2

Volume： 45

Page： 119-125

Cited Count：

WoS CC Cited Count：

SCOPUS Cited Count： 4

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 6

Affiliated Colleges：

材料科学与工程学院本学院/部未明确归属的数据

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