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研究生:朱盛煌
研究生(外文):Sheng-Huang Chu
論文名稱:以數位訊號處理平台分析適用於電子量測系統之遞迴式最小平方演算法
論文名稱(外文):The Analysis of the RLS Algorithm on DSP Platform for Electronic Weighing System
指導教授:賴永康
指導教授(外文):Yoeng-Kang Lai
學位類別:碩士
校院名稱:國立中興大學
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:2004
畢業學年度:92
語文別:英文
論文頁數:81
中文關鍵詞:遞迴式最小平方電子秤自適應型濾波器
外文關鍵詞:RLSadaptive filterweighing system
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在此論文中主要提出自適應性濾波器中的遞迥式最小平方演算法,來濾除電子量測系統的雜訊。輸入訊號為電子量測系統中的量測感測元,經由類比數位轉換器進行輸出。由於類比數位轉換器內部所提供的可程式化濾波器濾除雜訊極限為7Hz,在面對量測系統精度的提升上有一定的瓶頸存在,所以必須在類比數位轉換器後加一級數位濾波器。
自適應型濾波器能有效的消除雜訊,但自適應性濾波器的運算量龐大,傳統的8051處理器無法有效率的來執行此演算法,故利用數位訊號處理器做為核心的平台以增加運算的速度,藉助數位訊號處理器強大的運算能力提升驗證自適應型濾波器演算法的正確性。
本論文利用數位訊號處理器將遞迥式最小平方演算法實現,不但能增加電子秤的穩定度及解析度,亦能夠達到業界及時顯示的要求。

In this thesis, a method to eliminate noise in electronic weighing system with recursive least square algorithm in adaptive filter is proposed. The processed load cell signal on analog-to-digital converter in electronic weighing system is the input signal. The limi-tation of eliminating noise is 7Hz in the programmable digital filter of analog-to-digital converter. In order to increase electronic weighing system accuracy, we must add a digital filter behind analog-to-digital converter.
Adaptive filter can eliminate noise effectively. Adaptive filter algorithm is more complex and traditional 8051 processor can not execute this algorithm effectively. Therefore, it is necessary to use DSP processor to increase computing performance in the new experiment platform. Depending on the powerful computing ability of DSP processor, we can implement recursive least square algorithm and verify RLS algorithm on it.
In this thesis, to implement RLS algorithm on DSP processor not only increases the accuracy and stability of electronic weighing system but also reaches the demand for real-time display in the industrial applications.

1 Introduction - 1 -
1.1 Motivation - 1 -
1.2 Related Work - 2 -
1.3 Thesis Goals and Objective - 3 -
1.4 Thesis Organization - 4 -
2 Adaptive filter - 5 -
2.1 Introduction - 5 -
2.2 Four Classes of Application - 9 -
2.3 Adaptive Noise Cancellation - 13 -
2.4 Mean ERGODIC Theorem - 14 -
2.5 Stochastic Model - 18 -
3 Recursive Least Square Algorithm - 25 -
3.1 Some Preliminaries - 25 -
3.2 The Exponentially Weighted Recursive Least-Squares Algorithm - 31 -
3.3 Selection of the Regularization Parameter - 34 -
3.4 Update Recursive for the Sum of Weighted Error Squares - 37 -
3.5 Convergence Analysis of the RLS Algorithm - 39 -
3.6 Summary of the RLS Algorithm - 47 -
4 Adaptive Filter Design for Electronic Weighing System - 52 -
4.1 Modeling Noise - 52 -
4.1.1 Additive White Gaussian Noise Model - 53 -
4.1.2 Hidden Markov Model - 53 -
4.1.3 Hidden Markov Model for Noise - 55 -
4.2 Noise analysis and characteristic - 56 -
4.3 Modify Adaptive Filter Block Diagram - 58 -
5 Performance Analysis - 64 -
5.1 DSP platform - 64 -
5.1.1 DSP - 65 -
5.1.2 AD7730 - 67 -
5.2 System establish with RLS algorithm - 69 -
5.3 Compare accuracy with other filter - 76 -
6 Conclusion - 81 -

[1] US3709309: ELECTRONIC WEIGHING SYSTEM WITH DIGITAL READOUT, Jan. 9, 1973
[2] US3677357: ELECTROMAGNETIC LOAD COMPENSATED WEIGHING AP-PARATUS INCLUDIND DAMPING, July 18, 1972
[3] Simon Haykin, Adaptive Filter Theory, Prentice-Hall, 2001, ISBN 0-13-090126-1.
[4] SAEED V. VASEGHI, Advanced Digital Signal Processing and Noise Reduction, WILEY, 2000, ISBN 0-471-62692-9
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[6] Analog Devices, “AD7730, 1V/5V, 50 A, 24Bit, Sigma-Delta ADC” Analog De-vices, Inc., 1998.
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[8] TMS320C6x Optimizing C Compiler User’s Guide, Texas Instruments, 1998.
[9] TMS320C62x/C67x Programmer’s Guide, Texas Instruments, 1998.
[10] M. Tariq, W. Balachandran, “Modeling the Dynamic Response Characteristics of a Robot-Checkweigher,” Singapore International Conference., vol. 1, pp. 358 — 362, Feb. 1992
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[13] M. Halimic, W. Balachandran, “Kalman filter for dynamic weighing system,” IEEE Industrial Electronics., vol. 2, pp. 786-791, Jul. 1995
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[15] M. Halimic, W. Balachandran, Y. Enab, “Fuzzy logic estimator for dynamic weighing system,” IEEE International Conference., vol. 3, pp. 2123-2129, Sep. 1996
[16] M. Halimic, W. Balachandran, F. Cecelja, “Enhanced adaptive network fuzzy in-ference system in checkweighing systems performance improvement,” IEEE In-strumentation and Measurement Technology Conference., vol. 2, pp. 1094—1097, May. 2003
[17] M. Halimic, W. Balachandran, F. Cecelja, M. Hodzic, “Performance improvement of dynamic weighing systems using linear quadratic Gaussian controller,” IEEE In-strumentation and Measurement Technology Conference., vol. 2, pp. 1537—1540, May. 2003
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[19] R. Jirawimut, P. Ptasinski, V. Garaj, F. Cecelja, W. Balachandran, “A method for dead reckoning parameter correction in pedestrian navigation system,” IEEE In-strumentation and Measurement Technology Conference., vol. 2, pp.209-215, Feb. 2003

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