Generation of Artificial Earthquake Accelerogram Compatible with Spectrum using the Wavelet Packet Transform and Nero-Fuzzy Networks
The principal purpose of this article is to present a new method based on Adaptive Neural Network Fuzzy Inference System (ANFIS) to generate additional artificial earthquake accelerograms from presented data, which are compatible with specified response spectra. The proposed method uses the learning abilities of ANFIS to develop the knowledge of the inverse mapping from response spectrum to earthquake records. In addition, wavelet packet transform is used to decompose specified earthquake records and then ANFISs are trained to relate the response spectrum of records to their wavelet packet coefficients. Finally, an interpretive example is presented which uses an ensemble of recorded accelerograms to demonstrate the effectiveness of the proposed method.
[1] Ghaboussi, J. and Lin, C. J. (1997) ÔÇÿÔÇÿNew method of generating spectrum compatible accelerograms using neural networks,--
Earthquake Engineering & Structural Dynamics 27, 377-396.
[2] Lin, C. J. and Ghaboussi, J. (2001) ÔÇÿÔÇÿGenerating multiple spectrum compatible accelerograms using stochastic neural networks,-- Earthquake Engineering & Structural Dynamics 30, 1021-1042.
[3] Lee, S. C., Han, S. W. (2002) 'Neural-Network-Based Models for
Generating Artificial Earthquakes and Response Spectra', Computer and
Structures, 80: 1627-1638.
[4] Rajasekaran, S., Latha, V., and Lee, S. C. (2006) ÔÇÿÔÇÿGeneration of artificial earthquake motion records using wavelets and principal
component analysis,-- Journal of Earthquake Engineering 10(5), 665-691.
[5] Ghodrati Amiri, G. Bagheri, A. (2008) 'Application of Wavelet
Multiresolution Analysis and Artificial Intelligence for Generation of Artificial Earthquake Accelerograms', Structural Engineering and
Mechanics, 28:2, 153-166.
[6] Ghodrati Amiri, G., Bagheri, A., Seyed Razaghi, S.A., (2009)
'Generation of Multiple Earthquake Accelerograms Compatible with
Spectrum via the Wavelet Packet Transform and Stochastic Neural
Networks', Journal of earthquake engineering, 13:7,899-915.
[7] Ghodrati Amiri, G., Shahjouie, A., Saadat S. and Ajallooeian, M. (2011)
'Hybrid Evolutionary-Neural Network Approach in Generation of Artificial Accelerograms Using Principal Component Analysis and Wavelet-Packet Transform', Journal of earthquake engineering,15:1,50-76.
[8] Matlab software 2010.
[9] L.A. Zadeh, (1965) 'Fuzzy sets' Information and Control, 8: 338-353.
[10] Mamdani, E. H., and Assilian, S. (1976) 'An experiment in linguistic
synthesis with a fuzzy logic controller', International Journal Man-
Machine Studies, 7: 1-13.
[11] Takagi, T. and Sugeno, M. (1983) 'Derivation of fuzzy control rules
from human operator's control actions', Proc. IFAC Symp. Fuzzy
Inform. Knowledge Representation Decision Anal., 55 - 60.
[12] Jang, J S. (1993) 'ANFIS: adaptive network based fuzzy inference
system', IEEE Trans. Syst. Man Cybe, 23 665-684.
[13] Daubechies, I. (1992) ÔÇÿÔÇÿTen lectures on wavelets,-- CBMS-NSF
Conference Series in Applied Mathematics, Montpelier, Vermont.
[1] Ghaboussi, J. and Lin, C. J. (1997) ÔÇÿÔÇÿNew method of generating spectrum compatible accelerograms using neural networks,--
Earthquake Engineering & Structural Dynamics 27, 377-396.
[2] Lin, C. J. and Ghaboussi, J. (2001) ÔÇÿÔÇÿGenerating multiple spectrum compatible accelerograms using stochastic neural networks,-- Earthquake Engineering & Structural Dynamics 30, 1021-1042.
[3] Lee, S. C., Han, S. W. (2002) 'Neural-Network-Based Models for
Generating Artificial Earthquakes and Response Spectra', Computer and
Structures, 80: 1627-1638.
[4] Rajasekaran, S., Latha, V., and Lee, S. C. (2006) ÔÇÿÔÇÿGeneration of artificial earthquake motion records using wavelets and principal
component analysis,-- Journal of Earthquake Engineering 10(5), 665-691.
[5] Ghodrati Amiri, G. Bagheri, A. (2008) 'Application of Wavelet
Multiresolution Analysis and Artificial Intelligence for Generation of Artificial Earthquake Accelerograms', Structural Engineering and
Mechanics, 28:2, 153-166.
[6] Ghodrati Amiri, G., Bagheri, A., Seyed Razaghi, S.A., (2009)
'Generation of Multiple Earthquake Accelerograms Compatible with
Spectrum via the Wavelet Packet Transform and Stochastic Neural
Networks', Journal of earthquake engineering, 13:7,899-915.
[7] Ghodrati Amiri, G., Shahjouie, A., Saadat S. and Ajallooeian, M. (2011)
'Hybrid Evolutionary-Neural Network Approach in Generation of Artificial Accelerograms Using Principal Component Analysis and Wavelet-Packet Transform', Journal of earthquake engineering,15:1,50-76.
[8] Matlab software 2010.
[9] L.A. Zadeh, (1965) 'Fuzzy sets' Information and Control, 8: 338-353.
[10] Mamdani, E. H., and Assilian, S. (1976) 'An experiment in linguistic
synthesis with a fuzzy logic controller', International Journal Man-
Machine Studies, 7: 1-13.
[11] Takagi, T. and Sugeno, M. (1983) 'Derivation of fuzzy control rules
from human operator's control actions', Proc. IFAC Symp. Fuzzy
Inform. Knowledge Representation Decision Anal., 55 - 60.
[12] Jang, J S. (1993) 'ANFIS: adaptive network based fuzzy inference
system', IEEE Trans. Syst. Man Cybe, 23 665-684.
[13] Daubechies, I. (1992) ÔÇÿÔÇÿTen lectures on wavelets,-- CBMS-NSF
Conference Series in Applied Mathematics, Montpelier, Vermont.
@article{"International Journal of Earth, Energy and Environmental Sciences:61471", author = "Peyman Shadman Heidari and Mohammad Khorasani", title = "Generation of Artificial Earthquake Accelerogram Compatible with Spectrum using the Wavelet Packet Transform and Nero-Fuzzy Networks", abstract = "The principal purpose of this article is to present a new method based on Adaptive Neural Network Fuzzy Inference System (ANFIS) to generate additional artificial earthquake accelerograms from presented data, which are compatible with specified response spectra. The proposed method uses the learning abilities of ANFIS to develop the knowledge of the inverse mapping from response spectrum to earthquake records. In addition, wavelet packet transform is used to decompose specified earthquake records and then ANFISs are trained to relate the response spectrum of records to their wavelet packet coefficients. Finally, an interpretive example is presented which uses an ensemble of recorded accelerograms to demonstrate the effectiveness of the proposed method.
", keywords = "Adaptive Neural Network Fuzzy Inference System, Wavelet Packet Transform, Response Spectrum.", volume = "6", number = "8", pages = "538-4", }
