本論文是用來改善自適應共振網路（ART neural network）對非完整樣本的辨識能力。由於自適應共振網路為一非指導式、競爭式學習的類神經網路模型，使得非完整樣本容易對訓練效果造成負面影響。
自適應共振理論（Adaptive Resonance Theory, ART）對「穩定性—可塑性」的兩難問題（stability-plasticity dilemma）提供了一個解決方法。但是非指導式學習的方式無法分辨訓練樣本的種類，將可能降低了分類辨識碼的精確度。
本論文的方法是改良自適應共振網路，以指導式學習的方式，使自適應共振網路對不同的訓練樣本有不同的學習反應，並使用歸屬函數對權重做模糊調整。改良之後的自適應共振網路能對標準樣本產生精確的分類辨識碼來做記憶，而對非完整樣本做模糊的關連性學習，達到對非完整樣本進行辨識的功能，並藉由模糊權重調整提高辨識的準確度與速度。由於以自適應共振網路為基礎，能解決「穩定性¬—可塑性」的兩難問題。
根據模擬結果，本論文所提的方法對非完整樣本有快速且準確的學習與辨識能力。

This thesis proposes an enhanced adaptive resonance theory (ART) neural network to improve the capability of recognizing incomplete patterns.

ART provides a solution to the stability-plasticity dilemma. Nonetheless, the unsupervised learning algorithm can not distinguish standard patterns from the incomplete patterns during learning stage due to its unsupervised and competitive learning nature, which greatly degrades the accuracy rate of recognition.

The core ideas of the proposed approach in this thesis are: (1) Enhancing the ART neural network by supervised learning algorithm to create the capability of accepting both complete and incomplete learning patterns; and (2) Applying the concept of membership function in fuzzy theory to weight adjustment for network nodes to increase the accuracy rate of recognition. The enhanced ART is able to not only precisely memorize the classification codes of standard patterns but also learn fuzzy relationships for incomplete patterns.

The simulation results in this paper has shown the enhanced ART is able to learn and recognize incomplete patterns efficiently and correctly

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