Blind System Identification

Blind Estimation Using Higher-Order Statistics ◽

10.1007/978-1-4757-2985-6_3 ◽

1999 ◽

pp. 103-165

Author(s):

J. K. Richardson ◽

A. K. Nandi

Keyword(s):

System Identification ◽

Blind System Identification

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Blind system identification based on the complex cepstrum of the cyclic autocorrelation

1993 IEEE International Symposium on Circuits and Systems ◽

10.1109/iscas.1993.393824 ◽

2002 ◽

Cited By ~ 1

Author(s):

D. Hatzinakos

Keyword(s):

System Identification ◽

Complex Cepstrum ◽

Blind System Identification

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Blind system identification in an impulsive environment

Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics ◽

10.1109/host.1997.613521 ◽

2002 ◽

Author(s):

Jijun Yin ◽

A.P. Petropulu

Keyword(s):

System Identification ◽

Blind System Identification

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Blind System Identification Using Normalized Fourier Coefficient Gradient Vectors Obtained from Time-Frequency Entropy-Based Blind Clustering of Data Wavelets

Digital Signal Processing ◽

10.1006/dspr.1998.0317 ◽

1999 ◽

Vol 9 (1) ◽

pp. 18-35 ◽

Cited By ~ 2

Author(s):

B. Kaufhold ◽

R.L. Kirlin ◽

R.M. Dizaji

Keyword(s):

System Identification ◽

Fourier Coefficient ◽

Time Frequency ◽

Blind System Identification

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Matched Field Processing ‚Äî A Blind System Identification Technique

Electrical Engineering & Applied Signal Processing Series - Advanced Signal Processing Handbook ◽

10.1201/9781420037395.ch4 ◽

2000 ◽

Author(s):

N Ross Chapman ◽

Reza Dizaji ◽

R Kirlin

Keyword(s):

System Identification ◽

Matched Field Processing ◽

Identification Technique ◽

Blind System Identification

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Prediction of Occupancy Level and Energy Consumption in Office Building Using Blind System Identification and Neural Networks

10.1007/978-981-16-2778-1_3 ◽

2021 ◽

pp. 47-79

Author(s):

Jinshun Wu ◽

Yixuan Wei ◽

Xingxing Zhang

Keyword(s):

Neural Networks ◽

Energy Consumption ◽

System Identification ◽

Office Building ◽

Blind System Identification

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Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks

Applied Energy ◽

10.1016/j.apenergy.2019.02.056 ◽

2019 ◽

Vol 240 ◽

pp. 276-294 ◽

Cited By ~ 21

Author(s):

Yixuan Wei ◽

Liang Xia ◽

Song Pan ◽

Jinshun Wu ◽

Xingxing Zhang ◽

...

Keyword(s):

Neural Networks ◽

Energy Consumption ◽

System Identification ◽

Office Building ◽

Blind System Identification

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Noise robust blind system identification using second order statistics

Proceedings of IEEE. IEEE Region 10 Conference. TENCON 99. 'Multimedia Technology for Asia-Pacific Information Infrastructure' (Cat. No.99CH37030) ◽

10.1109/tencon.1999.818643 ◽

2003 ◽

Cited By ~ 1

Author(s):

M. Oshiro ◽

H. Ochi

Keyword(s):

System Identification ◽

Order Statistics ◽

Second Order ◽

Second Order Statistics ◽

Blind System Identification ◽

Noise Robust

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On the evaluation of multichannel blind system identification from the viewpoint of system equalization

2016 IEEE International Workshop on Acoustic Signal Enhancement (IWAENC) ◽

10.1109/iwaenc.2016.7602898 ◽

2016 ◽

Author(s):

Wancheng Zhang ◽

Patrick A. Naylor ◽

Zunwen He ◽

Yan Zhang

Keyword(s):

System Identification ◽

Blind System Identification

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Estimation and compensation of ultrasonic wavefront distortion using a blind system identification method

IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control ◽

10.1109/tuffc.2002.1009332 ◽

2002 ◽

Vol 49 (6) ◽

pp. 739-755 ◽

Cited By ~ 17

Author(s):

F. Lin ◽

R.C. Waag

Keyword(s):

System Identification ◽

System Identification Method ◽

Identification Method ◽

Wavefront Distortion ◽

Blind System Identification

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Multi-Channel Blind System Identification for Noninvasive Cardiovascular Monitoring

Advances in Bioengineering ◽

10.1115/imece2002-32580 ◽

2002 ◽

Author(s):

Yi Zhang ◽

Andrew T. Reisner ◽

H. Harry Asada

Keyword(s):

System Identification ◽

Cardiovascular System ◽

Cardiovascular Monitoring ◽

Identification Technique ◽

Blind System Identification ◽

Novel Method

A novel method for diagnostic monitoring of the cardiovascular system is presented, which combines peripheral measurements obtained continuously and non-invasively with a multi-channel blind system identification technique.

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