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題 名 | Modified Hamming Neural Network and Its Character Recognition System=改良型漢明類神經網路以及其字元辨認系統 |
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作 者 | 陳兩嘉; 張一全; | 書刊名 | 中原學報 |
卷 期 | 21 1992.12[民81.12] |
頁 次 | 頁125-135 |
分類號 | 448.6 |
關鍵詞 | 字元; 系統; 辨認; 漢明類神經網路; Perceptron網路; MAXNET網路; Perception net; Hamming neural network; MAXNET; |
語 文 | 英文(English) |
中文摘要 | 在此論文中,首先我們提出一個Perceptron網路的新轉移函數,來改善傳統漢明類神經網路的收斂速率。我們採用新的轉移函數的目的,是要加大Perceptron網路的漢明距離之比率,並加快辨識系統的收斂速率。另外,依據吾人理論的分析,我們修改MAXNET網路的函數,使新的網路比傳統漢明類神經網路具有更快的收斂速率。接下來,我們建立一個字元辨認系統,來測試改良型漢明類神經網路的效能。此一字元辨認系統能辨認26個英文字母和10個數字。經實驗結果證實,在相同的雜訊情況下,改良型漢明類神經網路遠優於傳統漢明類神經網路。 |
英文摘要 | In this paper, firstly, we propose a new transfer function of Perceptron net to improve the convergent speed of conventional Hamming neural network. The purpose of adopting the new transfer function is to magnify the ratio of the Hamming distance of Perception net and to increase the speed of recognition system. Secondly, based on our theoretical analysis, we modify the functions of MAXNET to make the network convergence much faster than that of conventional Hamming network. For testing the performance of our modified Hamming neural network, we establish a character recognitiion system which can recognize 26 English letters and 10 digits. From the simulation results, it shows that the convergent speed of our modified Hamming neural network is much superior to that of conventional Hamming neural network under the same testing condition. |
本系統中英文摘要資訊取自各篇刊載內容。