葉怡成;林冠呈;
頁: 171-190
日期: 2009/07
摘要: 本研究提出橢圓空間機率神經網路(Ellipse-Space Probabilistic Neural Networks, EPNN)，它擁有三種可透過訓練來修正的網路參數：代表輸入變數重要性的變數權值、代表樣本有效範圍的核寬倒數、及代表樣本可靠程度的資料權值。這些網路參數可以提升模型的準確度，並計算出重要性指標，以提供評估輸入變數重要性的能力。為證明此網路的性能，本研究以三個人為的分類問題以及七個實際的分類問題來做測試，並與倒傳遞網路及機率神經網路做比較。結果證明：(1)在人為的分類問題EPNN的模型準確度只略低於BPN，而遠高於PNN；在實際的分類問題EPNN的模型準確度明顯高於BPN與PNN。(2)重要性指標確實可以顯示輸入變數對輸出變數的重要程度，使模型具有解釋能力。
關鍵字: 類神經網路;機率神經網路;變數重要性;分類;

Ellipse-Space Probabilistic Neural Networks

Abstract: This study proposed the ellipse-space probabilistic neural network (EPNN), which includes three kinds of network parameters that can be adjusted through training: the variable weight representing the importance of each input variable of each pattern, the core-widthreciprocal representing the effective range of each pattern, and the data weight representing the reliability of each pattern. These network parameters can improve the accuracy of model, and calculate the variable importance index to offer the ability to appraise importance of each input variable. To prove the performance of EPNN, three artificial classification problems as well as seven actual classification problems were employed to test it and compare it with backpropagation network (BPN) and probabilistic neural network (PNN). The results proved that (1) the accuracy of EPNN is slightly lower than BPN, while strongly higher to PNN in the artificial classification problems; the accuracy of EPNN is obviously higher than BPN and PNN in the actual classification problems, and (2) the variable importance index really expressed the importance of each input variable to the output variable, which makes model have the explanation ability.
Keywords: artificial neural network;probabilistic neural network;variable importance;classification;

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