跳到主要內容

臺灣博碩士論文加值系統

(3.236.84.188) 您好!臺灣時間:2021/08/03 17:14
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:陳銘傑
研究生(外文):Ming-Jie Chen
論文名稱:基於雙算術邏輯運算處理器之模擬器設計
論文名稱(外文):Dual-ALU-Processor Simulator Design And Its Implementation Of Fuzzy Neural Networks
指導教授:吳俊德吳俊德引用關係
指導教授(外文):Gin-Der Wu
口試委員:莊家峰洪志偉
口試委員(外文):Chia-Feng JuangJeih-weih Hung
口試日期:2012-07-10
學位類別:碩士
校院名稱:國立暨南國際大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:46
中文關鍵詞:模擬器雙算術邏輯運算單元處理器模糊類神經網路
外文關鍵詞:Simulatordual arithmetic and logic unit processorfuzzy neural network
相關次數:
  • 被引用被引用:0
  • 點閱點閱:124
  • 評分評分:
  • 下載下載:22
  • 收藏至我的研究室書目清單書目收藏:0
本篇論文為設計出一套處理器之模擬器,並應用在模糊類神經網路上。此模擬器的功能在於模擬實驗室已設計出的雙算術邏輯運算單元處理器,指令包含資料轉換、布林運算、四則運算、雙算術邏輯運算、旋轉位移、控制指令,並能在電腦視窗中顯示當時佔存器和記憶體的動作,使在程式設計上能即時看到處理器內部運算現況,與工作站相比,減少了相當多的時間,對往後嵌入式系統設計應用上方便許多。

接著,以模擬模糊類神經網路在處理器執行的效果做為驗證。程式設計撰寫上,使用雙算術邏輯運算單元此特殊架構的平行運算能力,可有效將運算工作分配給兩顆ALU,達到減少程式行數、增加硬體處理速度,並實現模糊類神經網路在硬體上的運作。

This thesis is to design a processor simulator, and application of fuzzy neural network. The functions of this simulator is the simulation laboratory has been designed to double the arithmetic and logic unit processor, the instruction includes data conversion, Boolean operations, four operations, two-arithmetic and logical operations, rotation, displacement, control instructions, and in the computer window Display account for deposit and memory of the action, so that programming can immediately see the current status of the processor's internal operations, and workstations, to reduce a considerable amount of time a lot easier for the next embedded system design applications.
Then, as the effect of the implementation of the analog fuzzy neural network processor verification. Parallel computing power, programming written on this particular architecture of the dual arithmetic logical unit can effectively computing tasks assigned to the two The ALU, fuzzy neural network to reduce the program the number of rows, increasing the processing speed of hardware, and to achieve hardware operation.

誌謝 …………………………………………………………………………………i
論文摘要 …………………………………………………………………………ii
英文摘要……………………………………………………………………………iii
目錄 …………………………………………………………………………………iv
圖目錄 ………………………………………………………………………………vi
表目錄………………………………………………………………………………vii

第一章 緒論 …………………………………………………………………………1
1-1 研究動機 ……………………………………………………………… 1
1-2 雙算術邏輯處理器的介紹 …………………………………………… 2
1-3 模糊神經的預覽 ……………………………………………………… 6
1-4 論文的組織 ……………………………………………………………7
第二章 雙算術邏輯運算處理器…………………………………………………… 8
2-1模擬器 …………………………………………………………………8
2-2處理器指令集 …………………………………………………………9
2-3 模擬器的介面…………………………………………………………16
第三章 模糊神經網路的模擬器的實現……………………………………………17
3-1模糊神經網路的結構和算法 …………………………………………17
3-1.1 模糊神經網路的結構 ………………………………………18
3-1.2 結構學習………………………………………………………21
3-1.3 學習的規則……………………………………………………23
3-2 程序的硬體架構………………………………………………………27
3-3 RAM配置 ………………………………………………………………29
3-4特殊運算的操作……………………………………………………… 30
3-4.1定點 …………………………………………………………… 30
3-4.2平方根 ………………………………………………………… 32
3-4.3 Exponential ……………………………………………….… 35
第四章 結果…………………………………………………………………………36
4-1模糊神經網路的執行結果 …………………………………………………… 36
4-2程式結果 ……………………………………………………………………… 37
第五章 結論…………………………………………………………………………44
參考文獻 ……………………………………………………………………………45


圖目錄
圖1.2-1處理器架構…………………………………………………………………2
圖1.2-2雙ALU的架構………………………………………………………………3
圖1.2-3在每個階段的流水線業……………………………………………………5
圖2.1模擬器流圖 ………………………………………………………………… 8
圖2.2模擬器的界面 ………………………………………………………………16
圖3.1模糊神經網路的結構 ……………………………………………………… 17
圖3.2-1測試流程圖……………………………………………………………… 27
圖3.2-2學習流程圖……………………………………………………………… 28
圖3.3 RAM的配置………………………………………………………………… 29
圖3.4-1定點格式………………………………………………………………… 30
圖3.4-2乘法運算………………………………………………………………… 31
圖3.4-3除法運算………………………………………………………………… 31
圖3.4-4平方根流程圖…………………………………………………………… 32
圖3.4-5平方根操作……………………………………………………………… 34
圖3.4-6 Exponential操作……………………………………………………… 35
圖4.1模糊神經網絡的模擬結果………………………………………………… 36


表目錄
表1.2-1 暫存器指令 ……………………………………………………………… 4
表2.2-1數據傳輸指令格式 ……………………………………………………… 9
表2.2-2數據傳輸指令列表 ……………………………………………………… 10
表2.2-3布爾指令格式…………………………………………………………… 10
表2.2-4布爾指令列表…………………………………………………………… 10
表2.2-5 Add/Sub和DIV指令列表…………………………………………………11
表2.2-6 MAC指令列表 ……………………………………………………………12
表2.2-7雙ALU的指令格式……………………………………………………… 13
表2.2-8雙ALU指令列表………………………………………………………… 13
表2.2-9 Shift/Rotate指令格式 ……………………………………………… 14
表2.2-10 Shift/Rotate指令列表……………………………………………… 14
表2.2-11 Control的指令列表……………………………………………………15

[1]G. D. Wu,K. T. Kuo,“Dual-ALU structure processor for speech recognition,” System of Systems Engineering, 2006 IEEE/SMC International Conference on , pp. 193-196,April 2006

[2] C. T. Huang,P. C.Tseng,L. G. Chen, "Efficient VLSI architectures of lifting-based discrete wavelet transform by systematic design method," Circuits and Systems, 2002. ISCAS 2002. IEEE International Symposium on , vol.5, no., pp. V-565- V-568 vol.5, 2002, Taiwan, R.O.C., 2010

[3] G. D. Wu,Z. W. Zhu,"FFT-based daul-ALU processor for speech enhancement," Industrial Electronics, 2009. ISIE 2009. IEEE International Symposium on , vol., no., pp.800-803, 5-8 July 2009

4] G. D. Wu,Y. M. Liu,"Radix-22 based low power reconfigurable FFT processor," Industrial Electronics, 2009. ISIE 2009. IEEE International Symposium on , pp.1134-1138, 5-8 July 2009

[5] H. J. L,K. R.B,Smith, David R.; , "TOMAL: A Task-Oriented Microprocessor Applications Language," Industrial Electronics and Control Instrumentation, IEEE Transactions on , vol.IECI-22, no.3, pp.283-289, Aug. 1975

[6] M. J,S. N,A. R,"Adaptive predictive control with recurrent fuzzy neural network for industrial processes," Emerging Technologies & Factory Automation (ETFA), 2011 IEEE 16th Conference on , vol., no., pp.1-8, 5-9 Sept. 2011

[7] G. C,D. M,"A neural architecture for fast and robust face detection," Pattern Recognition, 2002. Proceedings. 16th International Conference on , vol.2, no., pp. 44- 47 vol.2, 2002

[8] Y. Z. Chun,L. H. Ji ,"Face detection based on SCNN and wavelet invariant moment in color image," Wavelet Analysis and Pattern Recognition, 2007. ICWAPR '07. International Conference on , vol.2, no., pp.783-787, 2-4 Nov. 2007

[9] O. S,L. T. H,B. K,"Neuro-fuzzy network for flavor recognition and classification," Instrumentation and Measurement, IEEE Transactions on , vol.53, no.3, pp. 638- 644, June 2004
[10] A. P,Y. R,"Simplified fuzzy rule-based systems using non-parametric antecedents and relative data density," Evolving and Adaptive Intelligent Systems (EAIS), 2011 IEEE Workshop on , vol., no., pp.62-69, 11-15 April 2011
[11] C. T. Lin,R. C. Wu,J. Y. Chang,S. F. Liang, "A novel prosodic-information synthesizer based on recurrent fuzzy neural network for the Chinese TTS system," Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on , vol.34, no.1, pp. 309- 324, Feb. 2004
[12] C. F. Juang,"A TSK-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms," Fuzzy Systems, IEEE Transactions on , vol.10, no.2, pp.155-170, Apr 2002

[13] H. M.H,"Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems [Book review]," Neural Networks, IEEE Transactions on , vol.7, no.5, pp.1316, Sep 1996
[14] R. L,C. K,"Designing and learning of adjustable quasi-triangular norms with applications to neuro-fuzzy systems," Fuzzy Systems, IEEE Transactions on , vol.13, no.1, pp. 140- 151, Feb. 2005
[15] M. Y. Chen,L. D. A,"Rule-base self-generation and simplification for data-driven fuzzy models," Fuzzy Systems, 2001. The 10th IEEE International Conference on , vol.1, no., pp.424-427, 2001

[16] N. A. V,B. P. K,"A Granular Reflex Fuzzy Min–Max Neural Network for Classification," Neural Networks, IEEE Transactions on , vol.20, no.7, pp.1117-1134, July 2009

QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top