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研究生:游佳燕
研究生(外文):Chia-Yen Yu
論文名稱:EvaluatingtheReliabilityofVotingSystemUsingtheMCS-RSMandNeuralNetwork
論文名稱(外文):Evaluating the Reliability of Voting System Using the MCS-RSM and Neural Network
指導教授:葉維彰葉維彰引用關係
指導教授(外文):Wei-Chang Yeh
學位類別:碩士
校院名稱:國立清華大學
系所名稱:工業工程與工程管理學系
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:英文
論文頁數:33
中文關鍵詞:可靠度權重式投票系統非權重式投票系統蒙地卡羅模擬法反應曲面法類神經網路
外文關鍵詞:ReliabilityWeighted Voting SystemUn-weighted Voting SystemMonte Carlo Simulation (MCS)Response Surface Methodology (RSM)Neural Network
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投票系統為一個進行投票行為的系統,其系統中包含了n 個單位。此篇論文中,將投票系統分為兩類,第一類為權重式投票系統,另一類為非權重式投票系統。投票系統中的每個單位在經過投票行為後都有一個二元(0或1)或棄權的決策產出。若權重式投票系統中決策為1的單位其所屬的權重加總後至少大於系統門檻值 乘上決策非棄權的單位所屬的權重加總值,則該投票系統的決策為1,相反的,其投票系統決策為0;而非權重式投票系統中,系統的每一個單位權重皆相等,因此,此投票系統將根據系統的單位數量做決策。當決策為1的單位數量至少大於系統門檻值乘上決策為非棄權的單位總數,則該非權重式投票系統決策為1,相反則為0。
在規劃、設計、控制系統的研究方面,關於計算、估計投票系統的可靠度是一項重要的議題。此篇論文中,蒙地卡羅模擬法是最先發展出用來計算非權重式投票系統可靠度,並且應用反應曲面法中Box-Behnken 設計法及類神經網路演算法用以求得其系統可靠度函數。在論文範例中得知,當使用類神經網路演算法求得其可靠度函數的效果優於使用反應曲面法中的設計法BBD。並且在章節的案例中應用其模擬法計算出台灣的總統大選選舉投票系統可靠度。
The voting system which has been studied normally consists of n units. It is divided into two types in this thesis. One is the weighted voting system, and the other is the un-weighted voting system. Each of these provides a binary decision (0 or 1), or a decision of abstaining from voting. The weighted voting system output is 1 if the cumulative weight of all 1-opting units is at least a pre-specified fraction of the cumulative weight of all non-abstaining units. Otherwise, the system output is 0. The un-weighted voting system output is 1 if the number of unit of all 1-opting decisions is at least a pre-specified fraction of the cumulative units of all non-abstaining ones.
Evaluating the reliability of the voting system is an important topic in the field of planning, designing and control. Compared with other studies in the field, in the present thesis, an intuitive Monte Carlo simulation (MCS) was first developed to find the estimated reliability of un-weighted voting system. Then, the response surface methodology (RSM) with the Box-Behnken design (BBD) and the algorithm of Neural Network are used to obtain the reliability function. In the case of the present study, using the Neural Network is more effective than using the BBD. In the last section, the reliability of a real case presidential election is evaluated.
Table of Contents
中文摘要 i
Abstract ii
Table of Contents iii
List of Figures v
List of Tables vi
Chapter 1 Introduction 1
1.1 Background 1
1.2 Significance and Motivation 2
1.3 Research Aims and Research Scope 2
1.4 Organization of Thesis 3
Chapter 2 Literature Review 5
2.1 Concept of general decision making system 5
2.2 K-out-of-N System 6
2.3 Voting System Reliability Evaluation 7
2.3.1 Problem Description 7
2.3.2 Methods of Evaluating Voting System Reliability 8
2.4 Voting System Reliability Model Application 11
Chapter 3 Reliability of Voting System Evaluating Model 13
3.1 voting system 13
3.1.1 Notations 13
3.1.2 Voting System introduce 14
3.1.3 Un-weighted Voting System reliability 15
3.1.4 Weighted Voting System reliability 17
3.2 Monte Carlo Simulation 19
3.2.1 Monte Carlo Simulation to Un-weighted Voting System 19
3.3 Response Surface Methodology 21
3.3.1 Response Surface Methodology to Un-weighted Voting System 21
Chapter 4 Empirical Study 24
4.1 Problem Definition 24
4.2 MCS-RSM of first model design 25
4.3 MCS-RSM of second model design 27
4.4 Monte Carlo Simulation to the presidential election in Taiwan 28
Chapter 5 Conclusion 31
References 32
References
G. Levitin , A.Lisnianski “Reliability optimization for weighted voting system”. Reliab Engng Syst Safety, vol71, 2001, 131-138.
Lars Nordmann , Hoang Pham “Weighted Voting Systems”. IEEE Trans. Reliability, Vol48 , NO. 1,1999 MARCH
Wei-Chang Yeh “A MCS-RSM approach for network reliability to minimize the total cost”. Int J Adv Manuf Technol, vol 22, 2003, 681-688.
G. Levitin ,”Optimal unit grouping in weighted voting systems”. Reliab Engng Syst Safety, vol 72 ,2001, 179-191.
B. Parhami “Threshold voting is fundamentally simpler than plurality voting”. Int’l J. Reliability, Quality and Safety Eng’g, vol 1, num 1, 1994, 95-102.
Y. Ben-Dov “Optimal reliability design of k-out-of-n systems subject to two kinds of failure”. J. Operations Research Soc, vol 31, 1980, 743-748
J. Wu, R. Chen “Efficient algorithms for k-out-of-n & consecutive-weighted- k-out-of-n system”. IEEE Trans. Reliability, vol 43, 1994 Dec, 650-655.
H. Pham, M Pham ”Optimal design of {k,n-k+1} systems subject to two modes”, IEEE Trans. Reliability, vol 40, 1991 Dec, 559-562.
J. Wu, R. Chen “An algorithm for computing the reliability of a weighted k-out-of-n system”. IEEE Trans. Reliability, vol 43, 1994 Jun, 327-328.
H. Pham, D.M. Malon “Optimal design of systems with competing failure modes”, IEEE Trans. Reliability, vol 43, 1994 Jun, 251-254.
B. Parhami “Voting algorithms”. IEEE Trans. Reliability, vol 43, 1994 Dec, 617-629.
H. Pham “Optimal system size for k-out-of-n systems with competing failure modes”. Mathematical and Computer Modeling, vol 15, 1991, 77-82.
J. Biernat “The effect of compensating fault models on n-tuple modular redundant (NMR) system reliability”. IEEE Trans. Reliability, vol 43, 1994 Jun, 294-300.
H. Pham “Reliability analysis for dynamic configurations of systems with three failure modes”. Reliability Eng’g and System Safety, vol 63, 1999, 13-23.
B. Parhami “The parallel complexity and weighted voting”. Proc. Int’l Symp. Parallel and Distributed Computing and Systerms, 1991, 382-385.
D.M. Blough, G.F. Sullivan “Voting using predispositions”. IEEE Trans. Reliability, vol 43, 1994 Dec, 604-616.
F.P. Mathur, P.T. de Sousa “Reliability models of NMR systems”. IEEE Trans. Reliability, vol R-24, 1975 Jun, 108-113.
Gregory Levitin “Maximizing survivability of vulnerable weighted voting system”. Reliab Engng Syst Safety, vol 83, 2004, 17-26.
Harri Niska, Teri Hiltunen, Ari Karppinen, Juhani Ruuskanen, Mikko Kolehmainen “Evolving the neural network model for forecasting air pollution time series”. Engineering Applications of Artificial Intelligence , vol 17 ,2004, 159-167.
Douglas C. Montgomery(2005) Design And Analysis of Experiments ,Wiley New York.
Myers RH, Montgomery DC(1995) Response Surface Methodology-process and product optimization using designed. Wiley New York.
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