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論文中文名稱:應用動態調整新穎全域和弦搜尋演算法結合人工神經網路於減刑犯再犯之預測 [以論文名稱查詢館藏系統]
論文英文名稱:Applying Dynamic Adjusting NGHS-ANN in Predicting the Recidivism of Taiwanese Commuted Prisoners [以論文名稱查詢館藏系統]
院校名稱:臺北科技大學
學院名稱:管理學院
系所名稱:管理學院管理博士班
畢業學年度:107
畢業學期:第一學期
出版年度:107
中文姓名:施柏州
英文姓名:Po-Chou Shih
研究生學號:101749003
學位類別:博士
語文別:中文
口試日期:2018/10/09
論文頁數:87
指導教授中文名:邱垂昱;范書愷
口試委員中文名:邱垂昱;范書愷;田方治;簡禎富;梁韵嘉
中文關鍵詞:動態調整新穎全域和弦搜尋演算法人工神經網路預測減刑犯再犯
英文關鍵詞:Dynamic Adjusting Novel Global Harmony SearchArtificial Neural NetworkPredictionRecidivism
論文中文摘要:本研究提出一個新的演算法,稱之為動態調整新穎全域和弦搜尋演算法(Dynamic Adjusting Novel Global Harmony Search, DANGHS)。此演算法乃是結合動態調整參數(Dynamic Adjusting Parameters)之概念於新穎全域和弦搜尋演算法(Novel Global Harmony Search, NGHS)中,並藉由14個著名的最佳化題庫問題,驗證本研究所提出之方法優於其他四種演算法。然而,調整參數的方式不只一種。因此,本研究也針對16種參數調整策略進行實驗分析。
接著,本研究再以台灣減刑犯再犯率為研究議題,結合DANGHS與人工神經網路(Artificial Neural Network, ANN),建構一套動態調整新穎全域和弦搜尋演化人工神經網路(DANGHS-ANN)之減刑犯再犯率預測模型。並藉由k-fold法之重複實驗,驗證本研究所提出之預測模型優於另外五種預測模型。最後,從研究數據中發現以下三點結論。第一,不同的問題適用於不同的參數調整策略。第二,本研究所提出的DANGHS演算法,其求解能力與效率優於其他四種演算法。第三,本研究所提的DANGHS-ANN減刑犯再犯率預測模型,其預測誤判率和穩健性皆優於其他五種預測模型。
論文英文摘要:This research proposes a new algorithm called the Dynamic Adjusting Novel Global Harmony Search (DANGHS), which combines the concept of Dynamic Adjusting Parameters in a Novel Global Harmony Search (NGHS), and uses 14 famous benchmark continuous optimization problems to verify that the method proposed by this research is superior to 4 other algorithms. However, as there is more than one method for adjusting parameters, 16 dynamic adjustment strategies are also applied for experimental analysis.
Then, taking the recidivism rate of Taiwanese commuted prisoners as the research topic, this research combined DANGHS and Artificial Neural Network (ANN) to construct a set of DANGHS-ANN prediction model for the recidivism rate of commuted prisoners. Subsequently, the k-fold method was adopted for repeated experiments to verify that the prediction model proposed by this research is superior to 5 other prediction models. Finally, 3 conclusions were found from the research data. (1) Different problems are applicable to different parameter adjustment strategies. (2) In terms of the DANGHS algorithm, as proposed by this research, its solving ability and efficiency are superior to other 4 algorithms. (3) For the DANGHS-ANN prediction model regarding the recidivism rate of commuted prisoners, as proposed by this research, its prediction error rate and robustness are superior to 5 other prediction models.
論文目次:中文摘要 i
英文摘要 ii
致謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
第一章 緒論 1
1.1 研究背景與研究動機 1
1.2 研究目的與方法 4
1.3 研究限制與範圍 5
1.4 研究架構與流程 5
第二章 文獻探討 7
2.1 減刑與假釋 7
2.1.1 減刑 7
2.1.2 假釋 8
2.1.3 減刑、假釋、犯罪因素之相關文獻彙整 10
2.2 人工神經網路 17
2.2.1 人工神經網路簡介 17
2.2.2 人工神經網路架構 18
2.2.3 人工神經網路運作 21
2.2.4 人工神經網路分類 21
2.2.5 倒傳遞人工神經網路 23
2.2.6 不足學習與過度學習 27
2.2.7 人工神經網路與啟發式演算法之結合應用 28
2.3 和弦搜尋演算法 29
2.3.1 和弦搜尋演算法簡介 29
2.3.2 和弦搜尋演算法之流程 30
2.4 改良式和弦搜尋演算法 32
2.5 自適應全域最佳和弦搜尋演算法 32
2.5.1 自適應全域最佳和弦搜尋演算法簡介 32
2.5.2 自適應學習機制 33
2.5.3 自適應全域最佳和弦搜尋演算法之流程 34
2.6 新穎全域和弦搜尋演算法 35
第三章 研究方法與研究架構 38
3.1 動態調整新穎全域和弦搜尋演算法 38
3.1.1 動態調整新穎全域和弦搜尋演算法 38
3.1.2 參數調整策略 39
3.1.3 NGHS流程圖與演算步驟 42
3.2 應用DANGHS至連續型最佳化求解問題 45
3.3 應用DANGHS結合ANN至減刑犯再犯預測問題 50
3.3.1 資料來源與資料前處理 50
3.3.2 實驗架構 53
3.3.3 動態調整新穎全域和弦搜尋演化人工神經網路建構 54
第四章 實驗結果與分析 58
4.1 運算環境設定 58
4.2 連續型最佳化求解問題之實驗結果 58
4.3 減刑犯再犯預測問題之實驗結果 60
4.3.1 倒傳遞人工神經網路之最佳參數 60
4.3.2 DANGHS之最佳參數調整策略 60
4.3.3 六種減刑犯再犯預測模型之實驗結果 75
第五章 結論與建議 82
參考文獻 85
論文參考文獻:1. 賴擁連,「假釋出獄人監禁期間與再犯關聯性之探討」,警學叢刊,第三十七卷,第一期,2006,第161-182頁。
2. 張聖照,假釋受刑人再犯預測研究,博士論文,中央警察大學犯罪防治研究所,桃園,2007。
3. 盧怡君,假釋再犯特性與影響因素之性別差異分析,碩士論文,中央警察大學犯罪防治研究所,桃園,2011。
4. 李明謹,成年犯罪人再犯影響因素之追蹤研究,碩士論文,中央警察大學犯罪防治研究所,桃園,2009。
5. 齊沛瑜,再犯研究之後設分析,學士論文,天主教輔仁大學社會學系,台北,2011。
6. 鄒啟勳,我國減刑成效之評估研究-以2007年罪犯減刑為例,碩士論文,中央警察大學犯罪防治研究所,桃園,2010。
7. 減刑與假釋, 2017年2月1日,http://blog.udn.com/Horace2007/3791598,。
8. 中華民國法務部統計處,矯正統計,參訪日期:2017年2月1日http://www.rjsd.moj.gov.tw/RJSDWEB/Default.aspx。
9. 李啟瑞,應用倒傳遞類神經網路於台灣減刑犯再犯率預測系統架構之研究,國立臺北科技大學,工業工程與管理系碩士班,碩士學位論文,2014。
10. Zou, D., Gao, L., Li, S., Wu, J. and Wang, X., 2010, "A novel global harmony search algorithm for task assignment problem," The Journal of Systems and Software, vol. 83, no. 10, pp. 1678-1688.
11. Zou, D., Gao, L., Wu, J. and Li, S., 2010, "Novel global harmony search algorithm for unconstrained problems," Neurocomputing, vol. 73, pp. 3308-3318.
12. Zou, D., Gao, L., Wu, J., Li, S. and Li, Y., 2010, "A novel global harmony search algorithm for reliability problems," Comput. Ind. Eng., vol. 58, no. 2, pp. 307-316.
13. 黃家偉,應用約略集合理論結合關連法則於減刑犯再犯因素之研究,碩士論文,國立臺北科技大學工業工程與管理系所,臺北,2014。
14. 郭妍彣,應用動態調整新穎全域和弦搜尋演算法於台灣減刑犯再犯率預測系統之建構,國立臺北科技大學,工業工程與管理系碩士班,碩士學位論文,2017。
15. 陳玉書、連鴻榮、李明謹,成年假釋人再犯預測因子與假釋審查指標之建構,中央警察大學2009年犯罪防治研討會論文集,2009,第199-226頁。
16. 蔡馥璟,時間序列法於警力資源規劃之研究,碩士論文,國立成功大學資訊管理研究所,台南,2005。
17. 丁榮轟,我國重刑化假釋政策與假釋出獄人再犯歷程之研究,犯罪與刑事司法研究半年刊,第五期,2005,第143-189頁。
18. Carroll, J. S., Wiener, R. L., Coates, D., Galegher J. and Alibrio, J. J., 1982, "Evaluation, Diagnosis and Prediction in Parole Decision," Law & Society Review, vol. 17, no. 1, pp. 199-228.
19. Williams III, F. P., McShane M. D. and Dolny, H. M., 2000, "Predicting Parole Absconders," The Prison Journal, vol. 80, no. 1, pp. 24-38.
20. MacKenzie D. L. and Spencer, D. L., 2002, "The Impact of Formal & Social Controls on the Criminal Activities of Probationers," Journal of Research in Crime & Delinquency, vol. 39, no. 3, pp. 243-276.
21. Benda, B. B., 2003, "Survival Analysis of Criminal Recidivism of Boot Camp Graduates Using Elements From General & Developmental Explanatory Models," International Journal of Offender Therapy and Comparative Criminology, vol. 47, no. 1, pp. 89-110.
22. Trulson, C. R., Marquart, J. W. , Mullings J. L. and Caeti, T. J., 2005, "In Between Adolescence and Adulthood:Recidivism Outcomes of a Cohort of State Delinquents," Youth Violence and Juvenile Justice, vol. 3, no. 4, pp. 355-387.
23. Trulson, C. R., Delisi M. and Marquart, J. W., 2011, "Institutional Misconduct, Delinquent Background & Rearrest Frequency Among Serious & Violent Delinquent Offenders," Crime and Delinquency, vol. 57, no. 5, pp. 709-731.
24. Geem, Z. W., Kim, J. H. and Loganathan, G. V., 2001, "A new heuristic optimization algorithm: harmony search," Simul. Soc. Comput. Simul., vol. 76, no. 2, pp. 60-68.
25. 劉向邦,以和諧搜尋演算法為基礎之混合式全域搜尋演算法求解含凹形節線成本最小成本轉運問題之研究,碩士論文,國立中央大學土木工程學系,桃園,2008。
26. Valian, E., Tavakoli, S. and Mohanna, S., 2014, "An intelligent global harmony search approach to continuous optimization problems," Applied Mathematics and Computation, vol. 232, no. 1, pp. 670-684.
27. Lee K. S. and Geem, Z. W., 2005, "A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice," Comput Method Appl Mech Eng., vol. 194, pp. 3902-3933.
28. Mahdavi, M., Fesanghary, M. and Damangir, E., 2007, "An improved harmony search algorithm for solving optimization problems," Appl. Math. Comput., vol. 188, no. 2, pp. 1567-1579.
29. Pan, Q. K., Suganthan, P. N., Tasgetiren, M. F. and Liang, J. J., 2010, "A self-adaptive global best harmony search algorithm for continuous optimization problems," Appl. Math. Comput., vol. 216, no. 3, pp. 830-848.
30. Omran, M. G. H. and Mahdavi, M., 2008, "Global-best harmony search," Appl. Math. Comput., vol. 198, pp. 643-656.
31. 葉怡成,類神經網路模式應用與實作,台北:儒林出版公司,2009,第1-2至4-21頁。
32. Basheer, I. A. and Hajmeer, M., 2000, "Artificial neural networks: fundamentals, computing, design, and application," Journal of Microbiological Methods, vol. 43, pp. 3-31.
33. 葉怡成,類神經網路模式應用與實作,台北:儒林出版公司,2003。
34. Tavakoli, S., Valian, E. and Mohanna, S., 2012, "Feedforward neural network training using intelligent global harmony search," Evolving Systems, vol. 3, no. 2, pp. 125-131.
35. 劉昌誠、徐建業、陳炯旭、蕭百勝,「應用類神經網路建構防害性自主罪再犯預測模型之初步嚐試」,亞洲家庭暴力與性侵害期刊,第六卷,第一期,2010,第43-64頁。
36. Masters, T., 1994, Practical Neural Network Recipes in C++, Academic Press, Boston, MA.
37. Looney, C. G., 1996, "Advances in feedforward neural networks: demystifying knowledge acquiring black boxes," IEEE Trans. Knowledge Data Eng., vol. 8, no. 2, pp. 211-226.
38. Dowla, F. U. and Rogers, L. L., 1995, Solving Problems in Environmental Engineering and Geosciences with Artificial Neural Networks, Cambridge: MIT Press, MA.
39. Haykin, S., 1994, Neural Networks: A Comprehensive Foundation, New York: Macmillan.
40. Zupan, J. and Gasteiger, J., 1993, Neural Networks for Chemists: An Introduction, New York: VCH.
41. Chiu, C. Y., Fan, S. K. S., Shih, P. C. and Weng, Y. H., 2014, "Applying HBMO-Based SOM in Predicting the Taiwan Steel Price Fluctuation," International Journal of Electronic Business Management, vol. 12, no. 1, pp. 1-14.
42. Lee, Z. J., Ying, K. C., Chen, S. C. and Lin, S. W., 2010, "Applying PSO-based BPN for predicting the yield rate of DRAM modules produced using defective ICs," Int. J. Adv. Manuf. Technol., vol. 49, pp. 987-999.
43. Chiu, C. Y., Lin, Y., Chou, Y. H. and Shih, P. C., 2013, "Parameter Optimization for Microlens Arrays Fabrication Using Genetic Algorithms," Journal of the Chinese Society of Mechanical Engineers, vol.34, no.6, pp. 507-517.
44. Eusuff, M. M. and Kevin E. Lansey, K. E., 2003, "Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm," JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT, vol. 129, no. 3, pp. 210-225.
45. Mehrabian, A. R. and Lucas C., 2006, "A novel numerical optimization algorithm inspired from weed colonization," ECOLOGICAL INFORMATICS, vol. 1, pp. 355-366.
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