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研究生:鄭群星
研究生(外文):Cheng Chyun-Shin Hsu
論文名稱:基於類神經網路與灰色模型的容錯系統可靠度評估
論文名稱(外文):Reliability Evaluations of Fault-Tolerant Systems Based on Neural Network and Grey Mode
指導教授:吳傳嘉徐演政徐演政引用關係
指導教授(外文):Chwan-Chia WUYen-Tseng Hsu
學位類別:博士
校院名稱:國立臺灣科技大學
系所名稱:電機工程技術研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:1998
畢業學年度:86
語文別:中文
中文關鍵詞:可靠度灰色模型類神經網路馬可夫模型
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通常在容錯電腦的可靠度分析中,採用馬可夫(Markov)模型時,均將永久故障、暫態故障、間斷故障分開考慮。實際上電腦系統在故障時,有可能此三種故障狀態都同時發生。因此本論文針對電腦可靠度分析的馬可夫模型提出可同時包含永久故障、間斷故障、及暫態故障的一般化馬爾可夫模型。此外、我們利用馬可夫模型提出一新的類神經網路架構及訓練演算法,去評估容錯電腦系統之可靠度,只要將此系統設計下的期望可靠度,輸入此類神經網路中,在經過訓練演算法訓練,而且類神經網路收斂後,則系統之設計參數即可由此類神經網路之權重值而獲得,此種方法可適用於任何型態容錯系統之可靠度模型,由模擬結果顯示使用此基於類神經網路之可靠度模型會比其他方法容易收斂。 由於馬可夫模型的系統狀態方程式均為一階的線性方程式所組成,傳統系統的可靠度即由此狀態方程式組解出,通常使用這種方式在複雜的容錯系統中是非常的複雜及困難的,因此本論文又提出了使用GM(1,1)、DF-GM(1,1)及ERC-GM(1,1)的灰色模型去分析電腦系統的可靠度,它可以直接、簡單及容易的從一個方程式中獲得系統的可靠度。但是使用此種灰色模型法,在每一時階能獲得最小誤差的輸入資料數是不同的,因此我們設計了一順向類神經網路來配合灰色模型以提高其精確度,模擬結果顯示這種方式比單獨使用灰色模型的方式更有精確的效果。
Generally, the reliability evaluations of fault-tolerant computer system based on the Markov model have focused on the effect of permanent fault, intermittent fault, or transient fault, separately. However, the system fault may include the effects of permanent fault, transient fault, and intermittent fault simultaneously. In this dissertation, we propose a generalized Markov reliabil-ity model, which includes the effects of permanent fault, intermittent fault, and transient fault for reliability evaluations. We also provide a neural net-work and an improved training algorithm to evaluate the reliability of the fault-tolerant systems. The desired system reliability under design is fed into theneural network and when the neural network converges, the design parameters areextracted from the weights of the neural network. The simulation results show that the neuro-based reliability models can converge faster than the other methods.The system state equations for the Markov model are a set of the first-order linear differential equations. Usually, the system reliabiliy can be evaluated from the combination of state solutions. This technique is very complicated and very difficult in the complex fault-tolerant systems. In this dissertation, we present several Grey Models(GM(1,1), DF-GM(1,1) and ERC-GM(1,1)),which offer an one-equation solution, to evaluate the reliability of computer system. It can obtain the system reliability directly and easily. But the datanumber for grey model that gets minimal error is different in each time step.Therefore, a neural network is designed on the basis of the more accuracy prediction for the grey modeling to evaluate the reliability. Finally, the simulation results show that this technique is better than the GMs in accuracy.
COVER
中文摘要
ABSTRACT
誌謝
CONTENTS
LISTS OF FIGURES
LISTS OF TABLES
GLOSSARY OF SYMBOLS
CHAPTER 1
CHAPTER 2
2.1 Markov Reliability Model
2.2 Markov Model with PI Fault
2.3 Markov Model with PIT Fault
CHAPTER 3
3.1 Neural Markov Model
3.2 Neural Trianing Algorithm
3.3 Neural Models for Simplex System
3.4 Neural Models for Duplex System
3.5 Neural Models for TMR System
CHAPTER 4
4.1 Grey Modeling
4.2 GM (1.1)
4.3 DF-GM (1,1)
CHAPTER 5
5.1 System Architecture
5.2 Evaluation Method
5.3 Neural Training Algorithm
5.4 Simulation Results
CHAPTER 6
References
作者簡介
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