Technology and software to determine adequate normalized correlation matrices in the solution of identification problems

Authors

DOI:

https://doi.org/10.15587/1729-4061.2017.118265

Keywords:

real signal, noise, correlation function, normalized correlation matrix, equivalent correlation matrix, input signal, output signal

Abstract

Statistical methods are widely used in solving problems of automatic management of industrial objects, as they enable us to determine the dynamic characteristics during normal operation of objects. The statistical correlation method for determining these dynamic characteristics is based on the solution of an integral equation that includes the correlation functions RXX(iDt) and RXY(iDt) of the input X(iDt) and output Y(iDt) signals. It allows one to obtain the dynamic characteristics of an object without disturbing its regular operation mode. However, the application of these methods for constructing mathematical models of real-life industrial objects presents the following certain difficulty. Interferences and noises are imposed upon the useful signal, hindering the calculation of the estimates of their static characteristics. The paper presents one possible option of creating alternative methods and technologies for eliminating the error induced by noise during the formation of correlation matrices. The proposed algorithms allow for reducing these matrices to the similar matrices of useful signals.

It is demonstrated in the paper that in the traditional approach, due to the normalization of estimates in the diagonal elements of the correlation matrices, the noise-induced errors disappear, while appearing in the remaining elements. As a result, the expected effect of improving the conditionality from the transition to normalized correlation matrices is not achieved. The technology and software for eliminating this defect are proposed, despite the problems with matrix conditioning. A new software for the rapid formation and analysis of numerous computational experiments confirming the effectiveness of the developed technology is proposed

Author Biography

Ulkar Eldar Sattarova, Azerbaijan University of Architecture and Construction Ayna Sultanova str., 11, Baku, Azerbaijan, AZ 1073

PhD, Associate professor

Department of Information technologies and systems

References

  1. Tikhonov, A. N., Arsenin, V. Ya. (1979). Methods for solving III – posed problems. Moscow: Nauka, 288.
  2. Samarskiy, A. A. (1997). Introduction to numerical methods. Moscow: Nauka, 288.
  3. Solodovnikov, V. V. (1960). Statistical dynamics of linear automatic control systems. Moscow: Fizmatgiz, 665.
  4. Aliev, T. (2007). Digital Noise Monitoring of Defect Origin. Springer, 235. doi: 10.1007/978-0-387-71754-8
  5. Aliev, T. (2003). Robust Technology with Analysis of Interference in Signal Processing. Springer, 199. doi: 10.1007/978-1-4615-0093-3
  6. Bureyeva, N. N. (2007). Multivariate statistical analysis using the "statistica" PPP. Nizhny Novgorod, 114.
  7. Ifeachor, E., Jervis, B. (2004). Digital Signal Processing: A Practical Approach. Moscow – Saint Petersburg – Kyiv, 989.
  8. Magomedovna, A. P. (2012). Textbook on Multidimensional Statistical Methods. Makhachkala, 75.
  9. Syritsyn, T. A. (1981). Reliability of hydraulic and pneumatic lines. Mechanical engineering. Moscow, 249.
  10. Kuzmin, V., Kedrus, V. A. (1977). Fundamentals of information theory and coding. Kyiv, 279.
  11. Karpov, F. (1968). Calculation of urban electrical distribution networks. Moscow, 224.
  12. Horsthemke, W., Lefever, R. (1984). Noise – Induced Transitions. Theory and Applications in Physics, Chemistry, and Biology. Springer, 294. doi: 10.1007/3-540-36852-3
  13. Lewandowski, D., Kurowicka, D., Joe, H. (2009). Generating random correlation matrices based on vines and extended onion method. Journal of Multivariate Analysis, 100 (9), 1989–2001. doi: 10.1016/j.jmva.2009.04.008
  14. Schott, J. R. (1998). Estimating correlation matrices that have common eigenvectors. Computational Statistics & Data Analysis, 27 (4), 445–459. doi: 10.1016/s0167-9473(98)00027-9
  15. Bun, J., Bouchaud, J.-P., Potters, M. (2017). Cleaning large correlation matrices: Tools from Random Matrix Theory. Physics Reports, 666, 1–109. doi: 10.1016/j.physrep.2016.10.005
  16. Aliev, T. A., Rzayeva, N. E., Sattarova, U. E. (2017). Robust correlation technology for online monitoring of changes in the state of the heart by means of laptops and smartphones. Biomedical Signal Processing and Control, 31, 44–51. doi: 10.1016/j.bspc.2016.06.015
  17. Aliev, T. A., Abbasov, A. M., Guluyev, Q. A., Pashaev, F. H., Sattarova, U. E. (2013). System of robust noise monitoring of anomalous seismic processes. Soil Dynamics and Earthquake Engineering, 53, 11–25. doi: 10.1016/j.soildyn.2012.12.013
  18. Sattarova, U. E. (2017). Technology and Software for Calculating Correct Normalization of Correlation Functions. Advances in Intelligent Systems and Computing, 149–159. doi: 10.1007/978-3-319-68720-9_18

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Published

2017-12-13

How to Cite

Sattarova, U. E. (2017). Technology and software to determine adequate normalized correlation matrices in the solution of identification problems. Eastern-European Journal of Enterprise Technologies, 6(4 (90), 69–76. https://doi.org/10.15587/1729-4061.2017.118265

Issue

Section

Mathematics and Cybernetics - applied aspects