A Supervised Learning ART Network with Fuzzy Weight Adjustment for Pattern Recognition

• Main Authors: Shih-Chen Sun, 孫士宸
• Other Authors: Y.K. Yang
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/68j7s6

碩士 === 國立臺灣科技大學 === 電機工程系 === 94 === 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