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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳瑤明 | |
| dc.contributor.author | Jhang-Chong You | en |
| dc.contributor.author | 游章充 | zh_TW |
| dc.date.accessioned | 2021-06-13T04:26:01Z | - |
| dc.date.available | 2007-07-27 | |
| dc.date.copyright | 2006-07-27 | |
| dc.date.issued | 2006 | |
| dc.date.submitted | 2006-07-21 | |
| dc.identifier.citation | 1. J. Williams, “A thermoelectric cooler temperature controller for fiber optic lasers,” Linear Technology, Application Note 89, 2001.
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Gan, “A comparative study on CMAC and ANFIS for nonlinear system modeling,” The Int. Conf. on Computational Intelligence for Modelling, pp. 270-278, 2003. 31. J. Q. Li, J. Z. Liu, and Y. G. Niu, “Application of cerebellar model articulation control in reheated-steam system,” TENCON 2004. IEEE Region 10 Conference, Vol.4, pp. 578-580, 2004. 32. D. Marr, “A theory of cerebellar cortex,” J. Physiol. (London), Vol. 202, pp. 437-470, 1969. 33. W. T. Miller, F. H. Glanz, and L. G. Kraft, “Application of a general learning algorithm to the control of robotic manipulators,” The International Journal of Robotics Research, Vol. 6, No.2, pp.84-98, 1987. 34. R. O. Shelton, Peterson, and J. K., “Controlling a truck with an adaptive critic CMAC design,” Simulation, Vol. 58, No. 5, pp. 319-326, 1992. 35. Y. Iiguni, “Hierarchical image coding via cerebellar model arithmetic computers,” IEEE Trans. Image Processing, Vol. 5, No. 10, 1996. 36. J. S. Ker, Y. H. Kuo, R. C. Wen, and B. D. Liu, “Hardware implementation of CMAC neural network with reduced storage requirement,” IEEE Trans. Neural Network, Vol. 8, No. 6, pp. 1545-1556, 1997. 37. L. G. Kraft, E. An, and D. P. Campagna, “Comparison of CMAC controller weight update laws, Decision and Control,” Proceedings of the 28th IEEE Conference on Decision and Control, Vol. 2, pp. 1746-1747, 1989. 38. Y. Wong, and A. Sideris, “Learning convergence in the cerebellar model articulation controller,” IEEE Transactions on Neural Network , Vol. 3, No. 1, pp. 115-121, 1992. 39. D. E. Thompson, and S. Kwon, “Neighborhood sequential and random training techniques for CMAC,” IEEE Trans. Neural Networks, Vol. 6, No. 1, pp. 196-202, 1995. 40. C. S. Lin, and C. T. Chiang, “Learning convergence of CMAC technique,” IEEE Trans. Neural Networks, Vol. 8, No. 6, pp. 1281-1292, 1997. 41. S. H. Lane, D. A. Handelman, J. J. Gelfand, “Theory and development of higher-order CMAC neural networks,” IEEE Contr. Syst., Vol. 12, pp. 23-30, 1992. 42. N. E. Cotter, and O. N. Main, “A pulsed neural network capable of universal approximation,” IEEE Transactions on Neural Network, Vol. 3, No. 2, pp. 308-314, 1992. 43. C. T. Chiang, and C. S. Lin, “Integration of CMAC and radial basis function techniques,” IEEE International Conference on Intelligent Systems for the 21st, Vol. 4, pp. 3263-3268, 1995. 44. C. T. Chiang, and C. S. Lin, “CMAC with general basis functions,” Neural Network, Vol. 9, No. 7, pp. 1199-1211, 1996. 45. 張志鴻, “應用CMAC 於非線性控制問題,” 國立交通大學控制工程研究所碩士論文, 1994。 46. 陳佑充, “結合CMAC 神經網路與模糊邏輯以控制複雜的非線性系統,” 國立交通大學控制工程研究所碩士論文, 1994。 47. 陳志銘, “植基於遺傳演算法之小腦模型直流伺服馬達控制系統設計,” 國立台灣師範大學工業教育研究所碩士論文, 1997。 48. 黃昭諺, “間時滑動模式之可微分小腦模型控制器設計,” 國立台灣師範大學工業教育研究所碩士論文, 1997。 49. C. S. Lin, and H. Kim, “Selection of learning parameters for CMAC-based adaptive critic learning,” IEEE Transactions on Neural Network, Vol. 6, No. 3, pp. 642-647, 1996. 50. C. M. Chen, H. M. Lee, and Y. R. Hsieh, “A new learning model of hierarchical CMAC neural networks,” Proceedings of Fourth National Conference on Artificial Intelligence and Applications, pp. 17-22, 1999. 51. 洪欽銘, 陳志銘, 羅維恆, 黃昭諺, “採用無失真壓縮技術精簡小腦模型控制器聯想記憶體之研究,” 第五屆人工智慧與應用研討會, pp. 277-282, 2000. 52. S. Yao, and D. Zhang, “The learning convergence of CMAC in cyclic learning neural networks,” Proceedings of 1993 International Joint Conference on Neural Networks, Vol. 3, pp. 2583-2586, 1993. 53. F. C. Chen, and C. H. Chang, “Practical stability issues in CMAC neural network control systems,” Proceedings of the American Control Conference, 1994. 54. F. C. Chen, and C. H. Chang, “Practical stability issues in CMAC neural network control systems,” IEEE Transcations on Control Systems Echnology, Vol. 4, No. 1, 1996. 55. C. T. Chiang, and C. S. Lin, “CMAC with general basis functions,” Neural Network, Vol. 9, No. 7, pp. 1199-1211, 1996. 56. Y. Wong, and A. 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Chiang, “Learning convergence of CMAC technique,” IEEE Trans. Neural Networks, Vol. 8, No. 6, pp. 1281-1292, 1997. 64. W. C. Luo, and K. T. Song, “Selection of optimal learning rates in CMAC based control schemes,” Proceedings of the 2002 IEEE International Symposium, pp.212-216, 2002. 65. S. H. Lane, D. A. Handelman, and J. J. Gelfand, “Theory and development of higher-order CMAC neural networks,” IEEE Contr. Syst., Vol. 12, pp. 23-30, 1992. 66. C. T. Chiang and C. S. Lin, “CMAC with general basis functions,” Neural Network, Vol. 9, No. 7, pp. 1199-1211, 1996. 67. S. L. Meyer, “Data analysis for scientists and engineers,” Wiley, New York, 1995. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/33127 | - |
| dc.description.abstract | 雷射二極體作為光纖通訊中的光源，而最重要的特性是其波長的穩定。當雷射二極體在高功率的操作下，由於熱的產生導致溫度升高，影響了雷射二極體所發出之光功率及波長的變動。而熱流系統的溫度變化具有非線性及時變的特性，不易以傳統之控制方法取得其數學模型；因此，本文主要利用可微分小腦模型控制器( Differentiable Cerebellar Model Articulation Controller，DCMAC )來學習雷射模組之特性，進而控制其溫度。在結果中討論可微分小腦模型控制器的設計及其性能的改進。DCMAC與PID控制器、模糊控制器( Fuzzy Controller )作比較可發現，DCMAC於雷射模組的溫度控制，系統的安定時間短、穩態誤差以及過衝量小，比起其它控制方法具有較佳的性能。 | zh_TW |
| dc.description.abstract | Laser diode which acts as light sources in laser module is used in optical fiber communication. One of the most desired characteristics of laser diode is its wavelength stability. When the laser is operated at higher power, more heat is produced, which will induce higher temperature. Then it causes the variation of output power and wavelength of lasers diode. With the characteristic of the nonlinear time-varying thermo-fluid system, it is difficult to define a mathematic model for temperature control. Therefore, in this paper, a differentiable cerebellar model articulation controller (DCMAC) was used to learn the characteristics and control the temperature of laser module. The results demonstrated how to design a DCMAC and improve its performance. The comparison was also made between DCMAC, PID and Fuzzy controller. It showed that DCMAC has the better performance with smaller settling time, steady state error and overshoot in temperature control of laser modules. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T04:26:01Z (GMT). No. of bitstreams: 1 ntu-95-R93522303-1.pdf: 3707733 bytes, checksum: b1e75df429d041559de63266dd5be122 (MD5) Previous issue date: 2006 | en |
| dc.description.tableofcontents | 中文摘要………………………………………………….……………..i
英文摘要………………………………………………………………....ii 圖目錄…………………………………………………………….……..vi 表目錄…………………………………………………………………...ix 符號說明……………………………………………………………....…x 第一章 緒論 1 1-1前言………………………………………………………………. 1 1-2文獻回顧…………………………………………………………. 7 1-3研究目的…………………………………………………………13 第二章 光纖通訊與雷射二極體 14 2-1光纖通訊簡介……………………………………………………15 2-2雷射二極體………………………………………………………21 2-2.1雷射二極體之結構……………………………………….21 2-2.2雷射二極體之操作原理……………….…………………22 2-2.3雷射二極體之操作特性……………….…………………23 2-2.4雷射模組簡介………............……….……………………26 第三章 小腦模型控制理論 27 3-1 CMAC背景………………………………………………………27 3-2 CMAC研究發展……....…………………………………………29 3-3 CMAC基本架構…………………………………………………32 3-4 CMAC理論………………………………………………………34 3-4.1狀態空間的界定與量化……………………...…………..34 3-4.2記憶體的分割與一般化………………………….…...….34 3-4.3 CMAC回想演算法……………………………….………39 3-4.4 CMAC學習演算法….……………………………...…….39 3-5可微分小腦模型控制器...……………………………………….41 3-5.1 DCMAC的產生…………………………………………..40 3-5.2 DCMAC的結構……….........................…………...……..41 3-5.3學習過程中須更新的資料………………………...……..45 3-6小腦模型控制器之優缺點……………………………...……….46 3-6.1小腦模型控制器之優點………………………...………..46 3-6.2小腦模型控制器之缺點……………………………...…..46 3-7 DCMAC設計流程與訓練………………………………...……..47 第四章 實驗設備與方法 49 4-1實驗設備…………………………………………………………49 4-1.1硬體設備……………………………….…………………49 4-1.2軟體架構………………………………………………….52 4-2受控系統分析……………………………………………………56 4-2.1雷射二極體之外殼…………………………………...…..57 4-2.2致冷器的原理與特性………….…………………………59 4-2.3溫度量測…………….……………………………………61 4-3溫度控制系統之設計…………………………………………....64 4-3.1閉迴路回授控制系統之架構…………………………….64 4-3.2可微分小腦模型控制器之設計……………………...…..65 4-3.3溫度控制系統之設計流程……………………………….68 4-4實驗架構與方法…………………………………………...…….70 4-5系統之時間常數……………………………………………...….72 4-6溫度的誤差分析與校正……………………………………...….73 第五章 結果與討論 75 5-1實驗結果與分析…………………………………………………75 5-2線上學習…………………………………………………………79 5-3與其他控制器的比較……………………………………..…..…81 5-3.1比例-積分-微分控制器……………………………..…….81 5-3.2模糊控制器…………………………………………….....83 5-3.3控制結果比較……………………………………..….......88 5-4系統的穩定性與干擾排斥性…..………………………………..93 5-4.1系統的穩定性…………………..………………………...93 5-4.2系統的干擾排斥性………………..…………………..….93 5-5不同雷射模組之控制結果……………………..………………..95 第六章 結論 99 6-1結論……………………………..……………………………..…99 6-2建議……………………………………….……….…………....100 參考文獻 101 附錄一 蝶式封裝之雷射模組……………………………….….……109 附錄二 可程式電源供應器…………………………………………..111 附錄三 直流電源供應器……………………………….……….……112 附錄四 資料擷取設備……………………………………..…………113 附錄五 熱敏電阻之參數……………………………………………..115 附錄六 DCMAC之訓練資料……………………………….…..……116 | |
| dc.language.iso | zh-TW | |
| dc.title | 可微分小腦模型控制器於雷射二極體之溫控研究 | zh_TW |
| dc.title | Study on the Temperature Control of Laser Diode by Differentiable Cerebellar Model Articulation Controller | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 94-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 何清政,周賢福,鄭慶陽,傅武雄 | |
| dc.subject.keyword | 可微分小腦模型控制器,溫度控制,雷射二極體, | zh_TW |
| dc.subject.keyword | DCMAC,temperature control,laser diode, | en |
| dc.relation.page | 116 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2006-07-22 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 機械工程學研究所 | zh_TW |
| 顯示於系所單位： | 機械工程學系 | |
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