臺灣博碩士論文加值系統

我們運用蜂巢式類神經網路(Cellular neural network)於震測水平連接(Seismic Horizon Linking)及圖型識別(Pattern Recognition).在震測水平連接方面,我們經由建構數種不同的條件,以其形成的能量方程式和蜂巢式類神經網路之能量方程式做比較,完成網路訓練的過程(Training Process),然後我們利用這完成訓練的網路來處理震測水平連接的問題.在圖型識別的方面,我們將蜂巢式類神經網路設計成聯想記憶體(Associative Memory),然後再用它來辨識圖型.我們針對二值的圖型(Bipolar pattern)做分類辨識的運用.在我們的實驗中,不論震測水平連接或圖型識別,均有良好的實驗結果.

目 錄
中 文 摘 要…..…………………………………………………….…..i
英 文 摘 要……………..………………………………………….….ii
誌 謝…………………………..……………………………………….iii
目 錄…………………………………………………………………...iv
表 目 錄……………………………………………………………….vi
圖 目 錄……………………………………………………………...viii
第 一 章 概 論….……………………………………………1
1.1 動機………………………………………………1
1.2 論文架構..………………………………………..2
第 二 章 蜂巢式類神經網路於震測水平之連接…………..3
2.1 蜂巢式類神經網路………………….……….….3
2.2 運動方程式與能量之關係……..……………….6
2.3 震測水平連接………………………….………..9
2.4 例子…………………………………………….26
2.5 實驗…………………………………………….32
第 三 章 蜂巢式類神經網路於圖型之識別….…………..46
3.1 聯想記憶體……………………………………..46
3.2 運用聯想記憶體做圖型辨識…………………..63
3.3 例子……………………………………………..65
3.4 實驗……………………………………………..73
第 四 章 結論與討論……………..………………..……….. 82
REFRENCES…………………………………………………………………. 83

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