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.

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