網路可靠度可提供相當有用的決策支援資訊，其應用層面相當廣泛，藉由協助提升決策品質，進而達成目標管理，而可靠度相關演算法，發展迄今已相當多元，其中夾擠反應曲面法與類神經網路皆有效應用於網路可靠度之估計，有關夾擠反應曲面法簡述如下：此方法整合細胞自動機基礎之蒙地卡羅模擬法及BBD設計進行模擬實驗，並將模擬估計值區分為解析部分及隨機部分以夾擠估計區間，再運用反應曲面法估算網路可靠度。本研究將夾擠概念與蒙地卡羅模擬法、深度優先搜尋、倒傳遞類神經網路及田口方法結合，用於估算兩端點二元狀態之網路可靠度，根據標竿網路範例模擬實驗結果顯示，本方法之估計結果優於未加入夾擠概念之類神經網路；另本方法在不同標竿網路範例下，顯示大多數估計結果可優於夾擠反應曲面法。

Network reliability is very useful decision support information. The squeeze response surface methodology (SqRSM) and artificial neural networks (ANNs) are two of the most useful types of optimal algorithms to estimate network reliability for different kinds of network configurations. The SqRSM method integrates cellular automata (CA)-based Monte Carlo simulation (MCS) and the Box-Behnken design (BBD) to simulate symbolic networks. The estimate response of the MCS is then separated into analytical and stochastic components, and the response surface methodology (RSM) is used to build the approximate symbolic network reliability function (SNRF). In this study, the proposed squeeze ANN (SqANN) approach combines the squeezed concept with depth-first search (DFS)-based MCS, BBD, ANN, and Taguchi method (TM) to evaluate the two-terminal binary-state network reliability. According to the experimental results of the benchmark example, the comparison appears to support the superiority of the proposed SqANN method over the traditional ANN. The finding also suggests that the SqANN method is better than the SqRSM approach for most applications.

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