臺灣博碩士論文加值系統

在本論文中，結合了傳統倒傳遞類神經網路與量子演算法，提出一個具有分類特性之類神經網路。利用量子神經元量化之特性，解決複雜、未知、時變與非線性之訊號處理的問題。
本研究利用量子類神經網路具有分類功能之特性，針對台灣電力負載所產生之訊號，省略預先對輸入訊號分類、建立模糊歸屬函數…等前置作業，直接於網路中設定分類條件，以量子神經元多階之轉移函數，將輸入特徵向量做分類的動作。藉由這種轉換，量子類神經網路即可有特徵分類的效果。在網路訓練時，可加強各種不同之特徵向量之趨近，以達到降低誤差的目的。而本研究所提出的量子類神經網路使用於訊號預測，免除了繁複的前置作業，也加強了預測訊號的精確度。對於系統的性能有進一步的提昇。

In this thesis, the non-stationary signal prediction by using quantum neural network (QNN) is proposed. The signals with fuzziness are expected to be classified clearly for enhancing the learning efficiency of neural network due to the hidden units with various graded levels in QNN structure.
As we know, power load forecasting is a type of non-stationary signal processing. Such an environment of signal information is generally complex and ill defined; also the behavior of power load is time-varying and dynamic. It sometimes might make NN have an ill learning and then cause it to have an unsatisfactory performance. Therefore, in order to improve the accuracy of NN’s prediction when it is used to deal with such signals with high non-stationary, uncertainty and fuzziness, several pre-analysis works might needed. However, it is usually a very difficult work to design analysis system, if too many unknown or uncertain factors are involved in the signals desired to process.
In this research, QNN power load forecasting is developed. This model is expected to precisely capture the complex relationships among load and its possible influencing factors, such as weather information, time of day, season of year and so on. By passing the complex signal’s pre-analysis work, the QNN model can effectively be trained and then have a better performance than traditional NN has.

中文摘要………………………………………………………………………i
英文摘要……………………………………………………………………ii
目錄…………………………………………………………………………iv
圖目錄………………………………………………………………………vi
表目錄………………………………………………………………………ix
第一章 緒論…………………………………………………………………1
1.1 背景簡介…………………………………………………………1
1.2 研究動機…………………………………………………………1
1.3 論文架構…………………………………………………………2
第二章 類神經網路簡介……………………………………………………3
2.1 類神經網路基本架構……………………………………………3
2.2 倒傳遞類神經網路………………………………………………6
第三章 量子類神經網路……………………………………………………9
3.1 傳統使用一階S型轉移函數之類神經網路的缺點………………9
3.2 量子類神經網路…………………………………………………9
3.2.1 量子神經元………………………………………………………9
3.2.2 跳躍點與量子區間………………………………………………12
3.3 量子演算法………………………………………………………14
3.3.1 類神經網路之權值修正…………………………………………14
3.3.2 量子區間之修正…………………………………………………16
3.3.3 演算流程…………………………………………………………17
第四章 實驗模擬與討論…………………………………………………21
4.1 模擬訊號種類……………………………………………………21
4.2 台灣電力總負載預測……………………………………………21
4.2.1 分類方式1………………………………………………………24
4.2.2 分類方式2………………………………………………………32
4.3 台灣電力尖峰負載預測…………………………………………37
4.3.1 分類方式1………………………………………………………39
4.3.2 分類方式2………………………………………………………49
4.3.3 分類方式3………………………………………………………54
4.4 種類參考節點……………………………………………………56
4.4.1 電力總負載預測…………………………………………………57
4.4.2 電力尖峰負載預測………………………………………………60
第五章 結論與未來研究方向……………………………………………63
5.1 結論………………………………………………………………63
5.2 未來研究方向……………………………………………………63
附錄A ……………………………………………………………………65
附錄B ……………………………………………………………………68
參考文獻……………………………………………………………………70

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