一種基于灰色RBF神經(jīng)網(wǎng)絡(luò)的系統(tǒng)效能評估方法
2020年電子技術(shù)應(yīng)用第12期
劉俊卿,,劉 進,肖龍忠
武漢船舶通信研究所,,湖北 武漢430205
摘要: 為了解決組成復(fù)雜,、功能多樣,、貧樣本的系統(tǒng)的綜合效能評估問題,針對系統(tǒng)的效能評估指標(biāo)體系三層結(jié)構(gòu),,構(gòu)建了基于灰色理論,、RBF神經(jīng)網(wǎng)絡(luò)以及灰色RBF神經(jīng)網(wǎng)絡(luò)的系統(tǒng)效能評估模型,并通過仿真驗證了這種灰色RBF神經(jīng)網(wǎng)絡(luò)模型的精度要高于灰色模型和RBF神經(jīng)網(wǎng)絡(luò)模型,,可以準(zhǔn)確地對功能多樣,、組成復(fù)雜但是樣本少的系統(tǒng)進行綜合效能評估,。
中圖分類號: TN914;TP391
文獻標(biāo)識碼: A
DOI:10.16157/j.issn.0258-7998.201012
中文引用格式: 劉俊卿,,劉進,,肖龍忠. 一種基于灰色RBF神經(jīng)網(wǎng)絡(luò)的系統(tǒng)效能評估方法[J].電子技術(shù)應(yīng)用,2020,,46(12):107-110.
英文引用格式: Liu Junqing,,Liu Jin,Xiao Longzhong. A method of system effectiveness evaluation based on grey RBF neural network[J]. Application of Electronic Technique,,2020,,46(12):107-110.
文獻標(biāo)識碼: A
DOI:10.16157/j.issn.0258-7998.201012
中文引用格式: 劉俊卿,,劉進,,肖龍忠. 一種基于灰色RBF神經(jīng)網(wǎng)絡(luò)的系統(tǒng)效能評估方法[J].電子技術(shù)應(yīng)用,2020,,46(12):107-110.
英文引用格式: Liu Junqing,,Liu Jin,Xiao Longzhong. A method of system effectiveness evaluation based on grey RBF neural network[J]. Application of Electronic Technique,,2020,,46(12):107-110.
A method of system effectiveness evaluation based on grey RBF neural network
Liu Junqing,Liu Jin,,Xiao Longzhong
Wuhan Institute of Ship Communication,,Wuhan 430205,China
Abstract: In order to solve the problem of comprehensive effectiveness evaluation of the system with complex composition, diverse functions and poor samples, this paper constructs a system effectiveness evaluation model based on grey theory, RBF neural network and grey RBF neural network according to the three-tier structure of the system effectiveness evaluation index system. The simulation results show that the accuracy of the grey RBF neural network model is higher than that of the grey model and RBF neural network. Through the grey RBF neural network model, we can accurately evaluate the comprehensive effectiveness of the system with various functions, complex composition, few samples.
Key words : index system,;efficiency evaluation,;grey theory;RBF neural network,;grey RBF neural network
0 引言
隨著現(xiàn)代技術(shù)的高速發(fā)展,,系統(tǒng)的組成更加復(fù)雜,功能更加多樣化,,這使得系統(tǒng)的綜合效能評估變得尤為重要,,而如何對復(fù)雜的系統(tǒng)做出合理、有效,、正確的效能評估,,對改進系統(tǒng)的設(shè)計方案,增強系統(tǒng)的綜合性能顯得尤為重要,。而現(xiàn)實中一些特殊的系統(tǒng)具有樣本少,、組成復(fù)雜的特點,這使得對其進行效能評估變得非常困難,。
目前,,系統(tǒng)效能評估的常見方法包括專家法、層次分析法,、神經(jīng)網(wǎng)絡(luò)法,、模糊評價法、灰色關(guān)聯(lián)分析法等[1],。這些方法各有優(yōu)缺點和不同的適用情況,,本文先建立了一種系統(tǒng)效能評估體系結(jié)構(gòu),根據(jù)這個評估體系并針對系統(tǒng)樣本少的特點,,提出一種基于灰色RBF神經(jīng)網(wǎng)絡(luò)[2]的綜合效能評估方法,,可以快速,、精確地評估系統(tǒng)的綜合效能。
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作者信息:
劉俊卿,,劉 進,,肖龍忠
(武漢船舶通信研究所,湖北 武漢430205)
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