Adaptive neural network approach for nonlinearity compensation in laser interferometer

Gunhaeng Heo; Wooram Lee; Seungok Choi; Jeehyong Lee; Kwanho You

doi:10.1007/978-3-540-74829-8_31

Adaptive neural network approach for nonlinearity compensation in laser interferometer

Gunhaeng Heo, Wooram Lee, Seungok Choi, Jeehyong Lee, Kwanho You

Sungkyunkwan University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Scopus citations

Abstract

In this paper, we propose a compensation algorithm to reduce the nonlinearity error which is occurred in a heterodyne laser interferometer as a nano-meter scale measurement apparatus. In heterodyne laser interferometer, frequency-mixing is the main factor of nonlinearity error. Using an RLS algorithm, the nonlinearity compensation parameters are found to be used through geometric projection. With the roughly modified intensity signals from LIA, the back-propagation neural network algorithm minimizes the objective function to track the reference signal for learning period. Through some experiments, it is verified that the proposed algorithm can reduce nonlinear factors and improve the measurement accuracy of laser interferometer.

Original language	English
Title of host publication	Knowledge-Based Intelligent Information and Engineering Systems
Subtitle of host publication	KES 2007 - WIRN 2007 - 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks, Proceedings
Publisher	Springer Verlag
Pages	251-258
Number of pages	8
Edition	PART 3
ISBN (Print)	9783540748281
DOIs	https://doi.org/10.1007/978-3-540-74829-8_31
State	Published - 2007
Event	11th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2007, and 17th Italian Workshop on Neural Networks, WIRN 2007 - Vietri sul Mare, Italy Duration: 12 Sep 2007 → 14 Sep 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 3
Volume	4694 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2007, and 17th Italian Workshop on Neural Networks, WIRN 2007
Country/Territory	Italy
City	Vietri sul Mare
Period	12/09/07 → 14/09/07

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10.1007/978-3-540-74829-8_31

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Heo, G., Lee, W., Choi, S., Lee, J., & You, K. (2007). Adaptive neural network approach for nonlinearity compensation in laser interferometer. In Knowledge-Based Intelligent Information and Engineering Systems: KES 2007 - WIRN 2007 - 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks, Proceedings (PART 3 ed., pp. 251-258). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4694 LNAI, No. PART 3). Springer Verlag. https://doi.org/10.1007/978-3-540-74829-8_31

Heo, Gunhaeng ; Lee, Wooram ; Choi, Seungok et al. / Adaptive neural network approach for nonlinearity compensation in laser interferometer. Knowledge-Based Intelligent Information and Engineering Systems: KES 2007 - WIRN 2007 - 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks, Proceedings. PART 3. ed. Springer Verlag, 2007. pp. 251-258 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).

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title = "Adaptive neural network approach for nonlinearity compensation in laser interferometer",

abstract = "In this paper, we propose a compensation algorithm to reduce the nonlinearity error which is occurred in a heterodyne laser interferometer as a nano-meter scale measurement apparatus. In heterodyne laser interferometer, frequency-mixing is the main factor of nonlinearity error. Using an RLS algorithm, the nonlinearity compensation parameters are found to be used through geometric projection. With the roughly modified intensity signals from LIA, the back-propagation neural network algorithm minimizes the objective function to track the reference signal for learning period. Through some experiments, it is verified that the proposed algorithm can reduce nonlinear factors and improve the measurement accuracy of laser interferometer.",

author = "Gunhaeng Heo and Wooram Lee and Seungok Choi and Jeehyong Lee and Kwanho You",

year = "2007",

doi = "10.1007/978-3-540-74829-8\_31",

language = "English",

isbn = "9783540748281",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

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pages = "251--258",

booktitle = "Knowledge-Based Intelligent Information and Engineering Systems",

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note = "11th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2007, and 17th Italian Workshop on Neural Networks, WIRN 2007 ; Conference date: 12-09-2007 Through 14-09-2007",

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Heo, G, Lee, W, Choi, S, Lee, J & You, K 2007, Adaptive neural network approach for nonlinearity compensation in laser interferometer. in Knowledge-Based Intelligent Information and Engineering Systems: KES 2007 - WIRN 2007 - 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks, Proceedings. PART 3 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 4694 LNAI, Springer Verlag, pp. 251-258, 11th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2007, and 17th Italian Workshop on Neural Networks, WIRN 2007, Vietri sul Mare, Italy, 12/09/07. https://doi.org/10.1007/978-3-540-74829-8_31

Adaptive neural network approach for nonlinearity compensation in laser interferometer. / Heo, Gunhaeng; Lee, Wooram; Choi, Seungok et al.
Knowledge-Based Intelligent Information and Engineering Systems: KES 2007 - WIRN 2007 - 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks, Proceedings. PART 3. ed. Springer Verlag, 2007. p. 251-258 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4694 LNAI, No. PART 3).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Adaptive neural network approach for nonlinearity compensation in laser interferometer

AU - Heo, Gunhaeng

AU - Lee, Wooram

AU - Choi, Seungok

AU - Lee, Jeehyong

AU - You, Kwanho

PY - 2007

Y1 - 2007

N2 - In this paper, we propose a compensation algorithm to reduce the nonlinearity error which is occurred in a heterodyne laser interferometer as a nano-meter scale measurement apparatus. In heterodyne laser interferometer, frequency-mixing is the main factor of nonlinearity error. Using an RLS algorithm, the nonlinearity compensation parameters are found to be used through geometric projection. With the roughly modified intensity signals from LIA, the back-propagation neural network algorithm minimizes the objective function to track the reference signal for learning period. Through some experiments, it is verified that the proposed algorithm can reduce nonlinear factors and improve the measurement accuracy of laser interferometer.

AB - In this paper, we propose a compensation algorithm to reduce the nonlinearity error which is occurred in a heterodyne laser interferometer as a nano-meter scale measurement apparatus. In heterodyne laser interferometer, frequency-mixing is the main factor of nonlinearity error. Using an RLS algorithm, the nonlinearity compensation parameters are found to be used through geometric projection. With the roughly modified intensity signals from LIA, the back-propagation neural network algorithm minimizes the objective function to track the reference signal for learning period. Through some experiments, it is verified that the proposed algorithm can reduce nonlinear factors and improve the measurement accuracy of laser interferometer.

UR - https://www.scopus.com/pages/publications/38049120935

U2 - 10.1007/978-3-540-74829-8_31

DO - 10.1007/978-3-540-74829-8_31

M3 - Conference contribution

AN - SCOPUS:38049120935

SN - 9783540748281

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 251

EP - 258

BT - Knowledge-Based Intelligent Information and Engineering Systems

PB - Springer Verlag

T2 - 11th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2007, and 17th Italian Workshop on Neural Networks, WIRN 2007

Y2 - 12 September 2007 through 14 September 2007

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Heo G, Lee W, Choi S, Lee J , You K. Adaptive neural network approach for nonlinearity compensation in laser interferometer. In Knowledge-Based Intelligent Information and Engineering Systems: KES 2007 - WIRN 2007 - 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks, Proceedings. PART 3 ed. Springer Verlag. 2007. p. 251-258. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). doi: 10.1007/978-3-540-74829-8_31

Adaptive neural network approach for nonlinearity compensation in laser interferometer

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