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研究生: 鄭欽澤
研究生(外文): Chin-Tse Cheng
論文名稱: 運用正弦函數組合技術於小樣本職能治療推斷之研究
論文名稱(外文): Using a sinusoidal combining technology in a small sample in the study of Occupational Therapy
指導教授: 蔡東亦
指導教授(外文): Tung-I Tsai
學位類別: 碩士
校院名稱: 樹德科技大學
系所名稱: 資訊管理系碩士班
論文出版年: 2011
畢業學年度: 99
語文別: 中文
論文頁數: 55
中文關鍵詞: 職能治療褚氏日常生活評量表小樣本類神經網路
外文關鍵詞: occupational therapyDaily Living Function Scalesmall samplesNeural Network
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隨著科技的進步,醫療水準相對提高下,傷病死亡率隨之降低;同時也加快了人們生活的腳步,壓力也隨之增加。使職能治療近年來受到重視,由於病情資訊不公開原則,導致診療資訊難以取得;醫生專業經驗難以傳承,職能治療師所使用的「褚氏日常生活評量表」不得過度使用,造成收集的樣本數量有限,如何在樣本有限與經驗不足的情況下,達到準確判斷患者病況,便是許多職能治療師所關注的問題。
由於類神經網路系統(Neural Network)具有高記憶能力與高效率的學習模式,可模擬不同類型的問題,且類神經網路具有處理非線性問題之能力。然而,對於類神經網路系統而言,必須仰賴訓練樣本所提供的資訊建立學習模式,要建立一個精準的類神經網路系統,唯獨充足的訓練資料方可,但實際上常因原始資料筆數有限,使得模式的預測不穩定。
本研究使用正弦函數組合所產生的虛擬因子填補因樣本數不足而無法忠實呈現的資料;並且利用極端值理論方法預測母體值域及產生虛擬樣本;接下來以虛擬樣本建構一個適用於小樣本的類神經網路模組。


As technology improves and medical level raises, there will be a decrease of the mortality rate. It also causes people to get faster the pace of life and to increase the pressure. Therefore, occupational therapy has been paid attention to the masses gradually in recent years. Due to the principles, which clinic information is not open, it leads to acquire the information hardly. Hence, the experience of doctors is not easy to share. Daily Living Function Scale limits by the amount of collecting sample data because of not excessive using. It is a main issue that how to diagnose the diseases of patients accurately by lacking samples and experience.

As a result of high capability of memory and efficiency of learning models in Neural Network, it can simulate different types of problems and deal with non-linear problems. However, users must provide enough training samples to build up reliable learning modes. Unfortunately, in fact, the amount of training data is often limited to make the models unstably.

This study uses the combination of sinusoidal functions to generate virtual factors, which can unknown critical data by insufficient number of samples. To use the method of extreme value theory, it predicts the ranges of population and generates the virtual sample. The next step is to construct adaptive models of the neural network in small samples.


摘要  i
ABSTRACT  ii
誌謝  iii
目錄  iv
表目錄  vi
圖目錄  vii
第一章 緒論  1
1.1 研究動機  1
1.2 研究目的  4
1.3 研究架構  5
1.4 論文架構  6
第二章 文獻探討  7
2.1 心理疾病  7
2.1.1 精神病  7
2.1.2治療方法  9
2.2 量測方法  11
2.3 小樣本學習  13
2.3.1 虛擬樣本  13
2.3.2 資訊擴展  14
2.3.3 貝氏網路  22
2.3.4 支援向量機  24
2.4 類神經網路  26
2.4.1 類神經網路基本概念  26
2.4.2 類神經網路架構  27
2.4.3 倒傳遞網路  30
2.5 小結  31
第三章 研究方法  32
3.1 極端值理論  32
3.2 研究步驟  35
第四章 研究結果  45
第五章 結論與建議  50
參考文獻  53


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