1. bookVolume 9 (2019): Issue 1 (January 2019)
Journal Details
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Format
Journal
eISSN
2449-6499
First Published
30 Dec 2014
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4 times per year
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English
access type Open Access

A MLMVN with Arbitrary Complex-Valued Inputs and a Hybrid Testability Approach for the Extraction of Lumped Models Using FRA

Published Online: 20 Aug 2018
Volume & Issue: Volume 9 (2019) - Issue 1 (January 2019)
Page range: 5 - 19
Received: 28 May 2017
Accepted: 19 Oct 2017
Journal Details
License
Format
Journal
eISSN
2449-6499
First Published
30 Dec 2014
Publication timeframe
4 times per year
Languages
English
Abstract

A procedure for the identification of lumped models of distributed parameter electromagnetic systems is presented in this paper. A Frequency Response Analysis (FRA) of the device to be modeled is performed, executing repeated measurements or intensive simulations. The method can be used to extract the values of the components. The fundamental brick of this architecture is a multi-valued neuron (MVN), used in a multilayer neural network (MLMVN); the neuron is modified in order to use arbitrary complex-valued inputs, which represent the frequency response of the device. It is shown that this modification requires just a slight change in the MLMVN learning algorithm. The method is tested over three completely different examples to clearly explain its generality.

Keywords

[1] A. Hirose, Complex-Valued Neural Networks, 2nd Edn., Springer, Berlin, Heidelberg, 2012.10.1007/978-3-642-27632-3Search in Google Scholar

[2] Y.Nakano, A.Hirose, Improvement of plastic landmine visualization performance by use of ring-CSOM and frequency-domain local correlation, IEICE Transactions on Electronics, vol. E92-C, no. 1, pp. 102-108, Jan. 2009.10.1587/transele.E92.C.102Search in Google Scholar

[3] S. L. Goh, M. Chen, D. H. Popovic, K. Aihara, D. Obradovic and D. P. Mandic, Complex Valued Forecasting of Wind Profile, Renewable Energy, vol. 31, pp. 1733-1750, Sep. 2006.Search in Google Scholar

[4] A. Handayani, A.B.Suksmono, T.L.R.Mengko, and A.Hirose, Blood Vessel Segmentation in Complex-Valued Magnetic Resonance Images with Snake Active Contour Model, International Journal of EHealth and Medical Communications, vol. 1, no. 1, pp. 41-52, Jan. 2010.10.4018/jehmc.2010010104Search in Google Scholar

[5] G. Avitabile, B. Chellini, G. Fedi, A. Luchetta and S. Manetti, A neural architecture for the parameter extraction of high frequency devices, in Proc. of IEEE Int. Symposium on Circuits and Systems (ISCAS), Sidney, Australia, 2001, pp. 577-580.Search in Google Scholar

[6] V. Rashtchi, E. Rahimpour, and E. M. Rezapour, Using a genetic algorithm for parameter identification of transformer R-L-C-M model, Electrical Engineering, 88, no.5, 417-422, June 2006.10.1007/s00202-005-0303-5Search in Google Scholar

[7] A. Shinterimov, W. J. Tang, W. H. Tang, and Q. H. Wu, Improved modelling of power transformer winding using bacterial swarming algorithm and frequency response analysis, Electric Power Systems Research, 80, no. 9, 1111-1120, Sep. 2010.10.1016/j.epsr.2010.03.001Search in Google Scholar

[8] W. H. Tang, S. He, Q. H. Wu and Z. J. Richardson, Winding deformation identification using a particle swarm optimiser with passive congregation of power transformes. Int. J. of Innovations in Energy Systems and Power, 1(11), 46-52, 2006.Search in Google Scholar

[9] I. Aizenberg, A. Luchetta, S. Manetti and M.C. Piccirilli, “ System Identification using FRA and a modified MLMVN with Arbitrary Complex-Valued Inputs”, Proc. of the IEEE International Joint Conference on Neural Networks (IJCNN’16), Vancouver, July 2016, pp. 4404-4411.10.1109/IJCNN.2016.7727775Search in Google Scholar

[10] N.N. Aizenberg and I.N. Aizenberg, “CNN Based on Multi-Valued Neuron as a Model of Associative Memory for Gray-Scale Images”, Proceedings of the Second IEEE International Workshop on Cellular Neural Networks and their Applications, Munich, October 14-16, 1992, pp.36-41.Search in Google Scholar

[11] I. Aizenberg, and C. Moraga, Multilayer feedforward neural network based on multi-valued neurons (MLMVN) and a backpropagation learning algorithm, Soft Computing, 11, no. 2, 169-183, Jan. 2007.10.1007/s00500-006-0075-5Search in Google Scholar

[12] N. Sen and R. Saeks, Fault Diagnosis for Linear System via Multifrequency Measurement”, IEEE trans. Circuits and Systems, vol. CAS-26, pp.457 - 465, 1979.10.1109/TCS.1979.1084659Search in Google Scholar

[13] N. N. Aizenberg, L. Ivaskiv, D. A. Pospelov, and G.F. Hudiakov, Multivalued Threshold Functions. II. Synthesis of Multivalued Threshold Elements, Cybernetics and Systems Analysis, vol. 9, no. 1, pp. 61-77, Jan 1973.10.1007/BF01068667Search in Google Scholar

[14] I. Aizenberg,, C. Moraga, and D. Paliy, A Feedforward Neural Network based on Multi-Valued Neurons, In Computational Intelligence, Theory and Applications. Advances in Soft Computing, XIV, (B. Reusch - Ed.), Springer, Berlin, Heidelberg, New York, 2005, pp. 599-612.10.1007/3-540-31182-3_55Search in Google Scholar

[15] I. Aizenberg, I., Complex-Valued Neural Networks with Multi-Valued Neurons. Berlin: Springer-Verlag Publishers, 2011.10.1007/978-3-642-20353-4Search in Google Scholar

[16] I. Aizenberg, D. Paliy, J. Zurada, and J. Astola, Blur identification by multilayer neural network based on multivalued neurons, IEEE Transactions on Neural Networks, vol. 19, no. 5, 883-898, May 2008.10.1109/TNN.2007.91415818467216Search in Google Scholar

[17] I. Aizenberg, A. Luchetta and S. Manetti, S, A modified learning algorithm for the multilayer neural network with multi-valued neurons based on the complex QR decomposition, Soft Computing, vol. 16, no. 4, 563-575, Apr. 2012.10.1007/s00500-011-0755-7Search in Google Scholar

[18] N.V.Manyakov, I. Aizenberg, N. Chumerin, and M. Van Hulle, Phase-Coded Brain-Computer Interface Based on MLMVN, book chapter in Complex- Valued Neural Networks: Advances and Applications (A. Hirose – Ed.), Wiley, 2012, pp. 185-208.10.1002/9781118590072.ch8Search in Google Scholar

[19] I. Aizenberg, Hebbian and Error-Correction Learning for Complex-Valued Neurons, Soft Computing, vol. 17, no. 2, pp. 265-273, Feb. 2013.10.1007/s00500-012-0891-8Search in Google Scholar

[20] I. Aizenberg, Adjustments to the proofs of the convergence theorems, available online at http://www.eagle.tamut.edu/faculty/igor/CVNNMVN book Convergence Proofs Adjustments.htm (2013).Search in Google Scholar

[21] G. Fedi, A. Luchetta, S. Manetti, and M. C. Piccirilli, A new symbolic method for analog circuit testability evaluation, IEEE Transactions on Instrumentation and Measurement, vol. 47, no. 10, 554-565, Apr. 1998.10.1109/19.744205Search in Google Scholar

[22] A. Liberatore, S. Manetti, and M. C. Piccirilli, A new efficient method for analog circuit testability measurement. Proc. of IMTC’94, Hamamatsu, Japan, pp. 193-196, 1994.Search in Google Scholar

[23] G. Fedi, S. Manetti, M. C. Piccirilli, and J. Starzyk, Determination of an optimum set of testable components in the fault diagnosis of analog linear circuits, IEEE Transactions on Circuits and Systems - Part I, 46, 779-787, Jul. 1999.10.1109/81.774222Search in Google Scholar

[24] S. Manetti, and M. C. Piccirilli, A singular-value decomposition approach for ambiguity group determination in analog circuits, IEEE Transactions on Circuits and Systems – Part I, vol. 50, no. 4, 477-487, Apr. 2003.10.1109/TCSI.2003.809811Search in Google Scholar

[25] G. Fontana, A. Luchetta, S. Manetti and M. C. Piccirilli An unconditionally sound algorithm for testability analysis in linear time-invariant electrical networks, Int. J. On Circuit Theory and Applications, vol. 44 no. 6, pp. 1308-1340, 2016.Search in Google Scholar

[26] G. Fontana, A. Luchetta, S. Manetti, M. C. Piccirilli, A Fast Algorithm for Testability Analysis of Large Linear Time-Invariant Networks, IEEE Trans. on Circuits and Systems – Part I, DOI: 10.1109/TCSI.2016.2645079, 2017.10.1109/TCSI.2016.26450792017Open DOISearch in Google Scholar

[27] R. Berkowitz, Conditions for network-elementvalue solvability, IRE Trans. Circ. Theory, vol. 9, pp. 24-29, 1962.10.1109/TCT.1962.1086882Search in Google Scholar

[28] W. J. Deika, A review of measures of testability for analog systems, Proc. Int. Autom. Test Conf. (AUTOTESTCON), 1977, pp. 279-284.Search in Google Scholar

[29] R. W. Priester, J. B. Clary, New measures of testability and test complexity for linear analog failure, IEEE Trans. Circuits Syst., vol. 30, pp. 884-888, 1981.10.1109/TC.1981.1675719Search in Google Scholar

[30] C. Lin, Z. F. Huang, R. Liu, Topological conditions for single-branch-fault, IEEE Trans. Autom. Control, vol. 28, pp. 689-694, 1983.10.1109/TAC.1983.1103302Search in Google Scholar

[31] G. N. Stenbakken, T. M. Souders, Test-point selection and testability measures via QR factorization of linear models, IEEE Trans. Instrum. Meas., vol. 36, pp. 406-410, 1987.10.1109/TIM.1987.6312710Search in Google Scholar

[32] G. N. Stenbakken, T. M. Souders and G. W. Stewart, Ambiguity groups and testability, IEEE Trans. Instrum. and Meas., vol. 38, pp.941-947, 1989.10.1109/19.39034Search in Google Scholar

[33] W. H. Huang and C. L. Wey, Diagnosability analysis of analogue circuits, Int. J. Circ. Theor. Appl., vol. 26, pp. 439–451, 1998.10.1002/(SICI)1097-007X(199809/10)26:5<439::AID-CTA23>3.0.CO;2-6Search in Google Scholar

[34] J. A. Starzyk and M. A. El-Gamal, Diagnosability of analog circuits-a graph theoretical approach, Proc. IEEE Int. Symp. Circuits and Systems, 1988, pp. 945-948.Search in Google Scholar

[35] B. Cannas, A. Fanni and A. Montisci, Testability evaluation for analog linear circuits via transfer function analysis Proc. IEEE Int. Symp. Circuits and Systems, 2005, pp. 992-995.Search in Google Scholar

[36] F. Grasso, A. Luchetta, S. Manetti, M. C. Piccirilli and A. Reatti, SapWin 4.0–a new simulation program for electrical engineering education using symbolic analysis, Computer Applications in Engineering Education, vol. 24 no. 1, pp. 44-57, 2016.10.1002/cae.21671Search in Google Scholar

[37] W. S. Bennett, Properly Applied Antenna Factors, IEEE Trans. on Electromagnetic Compatibility, EMC-28, 1, pp. 2-6, Feb. 1986.10.1109/TEMC.1986.4307232Search in Google Scholar

[38] G. Fedi, S. Manetti, G. Pelosi and S. Selleri, FEMtrained artificial neural networks for the analysis and design of cylindrical posts in rectangular waveguide, Electromagnetics, 22, 323–330, 2002.10.1080/02726340290083923Search in Google Scholar

[39] G. Pelosi, R. Coccioli, and S. Selleri, Quick finite elements method for electromagnetic waves, (pp. 89–113). London: Artech House, 1998.Search in Google Scholar

[40] Shinterimov, W.H. Tang, and Q.H. Wu, Transformer Core Parameter Identification Using Frequency Response Analysis, IEEE Trans. on Magnetics, vol. 46, pp. 141-149, January 2010.10.1109/TMAG.2009.2026423Search in Google Scholar

[41] D. Roger, E. Napieralska-Juszczak, and A. Henneton, High frequency extension of non-linear models of laminated cores, Int. J. Comput. Math. Electr. Electron. Eng., vol. 25, pp. 140-156, 2009.10.1108/03321640610634407Search in Google Scholar

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