本文選題：多代理系統(tǒng)　切入點(diǎn)：高斯概率模型　出處：《太原理工大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：通信技術(shù)和計(jì)算機(jī)技術(shù)的迅猛發(fā)展使網(wǎng)絡(luò)中的故障呈現(xiàn)復(fù)雜化、多樣化,而傳統(tǒng)的診斷方法和現(xiàn)有的智能診斷技術(shù)通常只能診斷出單一的故障類型和設(shè)備,已不能滿足目前網(wǎng)絡(luò)的需求。為解決網(wǎng)絡(luò)多重故障,可以基于單故障診斷領(lǐng)域研究和發(fā)展較為成熟的理論和技術(shù),進(jìn)一步研究與之相對(duì)應(yīng)的智能技術(shù),實(shí)現(xiàn)網(wǎng)絡(luò)故障診斷領(lǐng)域追求的高效且精確的診斷系統(tǒng)這一主要目標(biāo)。 在深入理解了生物免疫系統(tǒng)免疫機(jī)制的基礎(chǔ)上,進(jìn)一步研究了人工免疫系統(tǒng)的否定選擇算法,針對(duì)目前網(wǎng)絡(luò)發(fā)展階段的特點(diǎn),提出了一種以多代理技術(shù)為主的網(wǎng)絡(luò)故障診斷框架。該框架由兩個(gè)代理組成：中央免疫代理和本地診斷代理。中央免疫代理的作用是查看、管理和做出故障決策指令等。本地診斷代理由四個(gè)不同功能的模塊組成。當(dāng)本地診斷代理感知到外界有診斷請(qǐng)求傳入時(shí),啟動(dòng)相應(yīng)的診斷服務(wù)調(diào)度管理并協(xié)作,進(jìn)行握手后網(wǎng)絡(luò)信息采集和處理模塊采集本地的數(shù)據(jù),并進(jìn)行處理作為待檢測(cè)數(shù)據(jù),然后控制、管理模塊將自體與待檢測(cè)數(shù)據(jù)送到故障診斷模塊進(jìn)行診斷,并將診斷結(jié)果傳遞給故障響應(yīng)模塊,故障響應(yīng)模塊進(jìn)行相應(yīng)記錄后再將診斷結(jié)果通過調(diào)度握手后傳遞給中央免疫代理。 本文采用BP神經(jīng)網(wǎng)絡(luò)和證據(jù)理論的合成法則實(shí)現(xiàn)了故障診斷模塊中的多重故障診斷功能。為保證抗體多樣性,提高BP神經(jīng)網(wǎng)絡(luò)的識(shí)別精度,通過高斯人工免疫系統(tǒng)來求解BP神經(jīng)網(wǎng)絡(luò)的權(quán)值和偏差。對(duì)BP神經(jīng)網(wǎng)絡(luò)輸出節(jié)點(diǎn)的結(jié)果進(jìn)行規(guī)范化,并作為各故障類型的基本可信度分配,根據(jù)證據(jù)合成法則計(jì)算各故障類型合成后的基本可信度分配或信度函數(shù),最后通過判定條件實(shí)現(xiàn)對(duì)故障的最終判斷。 為獲取更準(zhǔn)確的網(wǎng)絡(luò)狀態(tài),不占用過多的網(wǎng)絡(luò)帶寬,在實(shí)驗(yàn)過程中應(yīng)用了動(dòng)態(tài)輪詢的方法采集網(wǎng)絡(luò)狀態(tài)信息。并對(duì)否定選擇算法和證據(jù)理論的閾值進(jìn)行了分析和研究,實(shí)驗(yàn)驗(yàn)證,本文所建框架對(duì)網(wǎng)絡(luò)多重故障的診斷切實(shí)可行。
[Abstract]:The rapid development of communication technology and computer technology make the faults in the network is complicated, diversified and intelligent diagnosis technology of traditional diagnostic methods and existing usually can diagnose the faults and single equipment, has been unable to meet the current network demand. In order to solve the multiple fault network, single fault diagnosis theory and technology the research and development of more mature based on the further study of the intelligent technology and the corresponding, the main goal of the field of network fault diagnosis, and accurate diagnosis system.
Based on deep understanding of the immune mechanism of biological immune system, further study of the negative selection algorithm of artificial immune system, according to the characteristics of network development, presents a multi agent technology based network fault diagnosis framework. The framework consists of two components: central immune agent agent and local diagnostic agent. The central immune agent acts as a check, management and make fault decision instructions. The local diagnostic agent consists of four different modules. When the local diagnostic agent perceived external diagnostic request comes in, start the corresponding diagnostic service scheduling management and collaboration, to shake hands after network information acquisition and processing module of the local collection data, as the data to be detected and processed, and then control the management module and the detection of autologous data to diagnose the fault diagnosis module, and the diagnosis. The result is passed to the fault response module, the fault response module is recorded and then passed to the central immune agent after the handshake.
The synthesis method of BP neural network and evidence theory to realize the function of fault diagnosis of multiple fault diagnosis module. In order to ensure the diversity of antibodies, improved BP neural network recognition accuracy, weights and biases to BP neural network for solving Gauss by artificial immune system. The output of the BP neural network node results standardization. And as the basic probability assignment of the fault type, according to the evidence combination rule calculation of each fault type after the synthesis of basic probability assignment or reliability function, finally determine the conditions to achieve the final judgment of the fault.
In order to obtain more accurate network state, do not take up too much network bandwidth, in the course of the experiment methods of collecting network state information. The application of dynamic polling and the negative selection algorithm and evidence theory threshold are analyzed and experimental verification, the framework for diagnosis of multiple fault network is feasible.

【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.06

【共引文獻(xiàn)】

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1 蘆天亮;基于人工免疫系統(tǒng)的惡意代碼檢測(cè)技術(shù)研究[D];北京郵電大學(xué);2013年

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1 付存君;基于Android平臺(tái)智能手機(jī)防火墻的應(yīng)用研究[D];重慶理工大學(xué);2013年

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本文編號(hào)：1626076