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研究生： 謝明哲
Ming-che Hsieh
論文名稱： 應用人工免疫系統為基礎之倒傳遞神經網路於無線射頻辨識系統定位之研究
Application of Artificial Immune System-Based Back Propagation Neural Network to RFID Positioning System
指導教授： 郭人介
Ren-jieh Kuo
口試委員: 許總欣
Tsung-shin Hsu
楊文鐸
Wen-dwo Yang
學位類別： 碩士
Master
系所名稱： 管理學院 - 工業管理系
Department of Industrial Management
論文出版年： 2010
畢業學年度： 98
語文別： 中文
論文頁數： 80
中文關鍵詞： 無線射頻技術 、倒傳遞神經網路 、人工免疫系統 、物件追蹤 、路徑規畫
外文關鍵詞： radio frequency identification technology, back-propagation neural network, artificial immune systems, good tracking and picking, route plan
相關次數： 點閱：447  下載：0 
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    • 在供應鏈中，倉儲管理扮演著十分重要的角色，而倉儲管理又可區分為搬運、進貨、揀貨與出貨等活動。其中，揀貨則是倉儲活動中消耗人力與時間最多的項目。此外，隨著新的生產策略如JIT、週期時間縮短以及快速回應等的提出，另外還有緊急訂單的產生，以上這些狀況都使得揀貨活動的難度提升。因此，為了良好地做好倉儲管理，應用無線射頻技術於倉儲管理中，使得倉儲管理更有效率，成為一個新的思考方向。
    為有效提升撿貨之效率，本研究提出兩種以人工免疫系統為基礎之倒傳遞神經網路模型，第一種模式為利用人工免疫系統求取倒傳遞神經網路權重，第二種模式則是利用人工免疫系統來替倒傳遞神經網路作訓練前的特徵選擇，而後再以此兩種以人工免疫系統為基礎之倒傳遞神經網路，經由學習RSSI值與位置相互間之關係，用以預測撿貨車之位置。當新的訂單產生時，一旦取得撿貨車位置，即可透過分之界限法來規劃出最佳撿貨路徑。經由運算結果顯示，本研究所提出之兩種以人工免疫系統為基礎之倒傳遞神經網路相較於倒傳遞神經網路皆能提供較佳的預測效果。

    Warehouse management which can be divided into several components, such as transportation, procurement, delivery and other activities plays an important role in supply chain. Among various activities within warehousing, the process of good tracking and picking is the most labor intensive and time consuming. But with the application of new production strategies such as just in time, cycle time can be reduced and any response can be made quickly. In the situation where emergency orders are in place, the process of good tracking and picking will be more difficult. Therefore, in order to establish a good warehouse management, application of RFID technology in warehouse will make warehousing activities become more efficient and this will be a new innovation direction.
    To improve the effectiveness and efficiency of good tracking and picking process, this study first intends to propose two artificial immune system (AIS) based back-propagation neural networks (BPNNs). The first BPNN employs AIS to determine the optimal weights while the second BPNN applies AIS to make feature selection for BPNN. Then, these two AIS based BPNNs are used to learn the relation between RSSI values and position for predicting picking cart’s position. Once picking cart’s position is known, it can be utilized to plan the best rout for picking cart through branch and bound method if new order is coming. The computational results show that the proposed two AIS based BPNNs really can provide better prediction than traditional BPNN.

    摘要.............................................................i Abstract........................................................ii 誌謝...........................................................iii 目錄............................................................iv 表目錄..........................................................vi 圖目錄.........................................................vii 第一章 緒論.....................................................1 1.1 研究背景與動機.............................................1 1.2 研究目的...................................................2 1.3 研究限制...................................................2 1.4 研究流程...................................................3 第二章 文獻探討.................................................4 2.1 無線射頻辨識系統...........................................4 2.1.1 歷史...................................................4 2.1.2 組成元件...............................................7 2.1.3 相關研究與應用........................................10 2.2 倒傳遞神經網路與柔性運算..................................16 2.2.1 倒傳遞神經網路........................................16 2.2.2 柔性運算於倒傳遞神經網路之應用........................20 2.3 撿貨路徑規劃方法..........................................22 2.4 人工免疫系統..............................................23 2.4.1 人工免疫演算法........................................23 2.4.2 人工免疫系統之相關應用................................28 第三章 研究方法................................................30 3.1 資料蒐集..................................................31 3.2 資料轉換..................................................32 3.3 人工免疫系統為基礎之倒傳遞神經網路演算法..................33 3.3.1 AISBP.................................................35 3.3.2 FSBP..................................................39 3.4 驗證......................................................43 第四章 電腦模擬................................................44 4.1 函數模擬Booth.............................................45 4.1.1 田口式實驗（Taguchi Method）..........................46 4.1.1.1 BP................................................46 4.1.1.2 AISBP.............................................47 4.1.1.3 BP與AISBP學習收斂狀況.............................49 4.1.2 k重交叉驗證（k-fold cross validation）與比較..........50 4.1.2.1 BP................................................50 4.1.2.2 AISBP.............................................51 4.1.3 檢定..................................................52 4.2 函數模擬Matyas............................................53 4.2.1 田口式實驗（Taguchi Method）..........................54 4.2.1.1 BP................................................54 4.2.1.2 AISBP.............................................55 4.2.1.3 BP與AISBP學習收斂狀況.............................57 4.2.2 k重交叉驗證（k-fold cross validation）與比較..........58 4.2.2.1 BP................................................58 4.2.2.2 AISBP.............................................59 4.2.3 檢定..................................................60 第五章 實驗分析................................................61 5.1 實驗情境..................................................61 5.2 田口式實驗（Taguchi Method）..............................64 5.2.1 BP....................................................64 5.2.2 AISBP.................................................65 5.2.3 BP、FSBP與AISBP學習收斂狀況...........................67 5.3 k重交叉驗證（k-fold cross validation）與比較..............68 5.4 檢定......................................................70 5.5 路徑規劃..................................................72 第六章 結論與建議..............................................75 6.1 研究結論..................................................75 6.2 研究貢獻..................................................76 6.3 未來研究建議..............................................76 參考文獻........................................................77

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