長久以來分類模型都有廣泛的討論，而自動化分類模型更是在各種領域廣泛的運用。而放射狀基底函數網絡(Radial Basis Function network, 後稱RBF Network)在對於處理非線性系統有著良好的處理能力而廣泛的用來進行分類問題。但為了達到好的分類效果，建立RBF Network 模型會面臨許多參數的選擇，如決定隱藏層節點的個數、徑向基底函數參數設定以及最佳化參數值等問題。為了解決以上問題，我們提出一種新的RBF network的方法，對於解決隱藏層個數的問題，我們採用讓資料分布的狀況來決定隱藏層節點的個數，使用自適應分群演算法可以依照資料分布的特性來決定分群的群數，而RBF基底函數也從自適應分群演算法決定初始中心位置與標準差決定相似程度。我們也加入混合式的學習機制來達到網路的最佳化，包含最陡路徑倒傳遞法與最小平方法。我們的方法提供許多優勢，對於RBF網路中必須決定的隱藏層節點個數與徑向基底函數的參數都由訓練資料來決定，合理地建立模型的參數能夠提供一開始不錯的準確度，再經過最佳化參數的學習機制讓分類模型提升準確度。我們的方法能夠處理單一類別與多重類別的分類問題，並以多個實驗來顯示我們的效果。

Radial basis function (RBF) networks are popularly applied to solving pattern classification problems. However, several issues are encountered in the applications, including the determination of the number of hidden nodes, the initial settings of the basis functions, and the refinement of the parameter values. In this paper, we propose a novel RBF network approach for classification applications. An iterative self-constructing clustering algorithm is used to determine the number of hidden nodes in the hidden layer. Basis functions are generated, and their centers and deviations are initialized according to the data distribution of the formed clusters. To learn optimal values of the network parameters, a hybrid learning strategy, involving steepest descent back-propagation and least squares method, is adopted. Hyperbolic tangent sigmoid functions and Tikhonov regularization are employed. As a result, optimized RBF networks are obtained. Our approach can offer advantages in practicality. The number of hidden nodes and initial settings of the basis functions are determined automatically. Through the incorporation of adaptive deviations, data can be described more appropriately by the basis functions. The adopted optimization learning allows more accurate predictions to be made. Furthermore, our approach is applicable to both single-label and multi-label classification problems. A number of experiments are conducted to show the effectiveness of the proposed approach.

論文審定書. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
論文公開授權書. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
致謝. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
中文摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
英文摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
目錄. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
圖目錄. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
表目錄. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii
第一章緒論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 研究背景. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 研究動機. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 方法與論文架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
第二章文獻探討. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
第三章研究方法. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.1 樣式分類問題. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.2 方法架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.2.1 階段一、建構網路模型. . . . . . . . . . . . . . . . . . . . . . 10
3.2.2 階段二、參數最佳化. . . . . . . . . . . . . . . . . . . . . . . 17
3.2.3 方法總結. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.3 實例說明. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
第四章實驗與結果分析. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.1 評估指標. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.2 資料集. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.3 實驗一. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4.4 實驗二. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
4.5 實驗三. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
第五章結論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
參考文獻. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

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