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

Aizenberg, Igor; Luchetta, Antonio; Manetti, Stefano; Piccirilli, Maria Cristina

doi:10.2478/jaiscr-2018-0021

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.

References

[1] A. Hirose, Complex-Valued Neural Networks, 2nd Edn., Springer, Berlin, Heidelberg, 2012.10.1007/978-3-642-27632-3
Search in Google Scholar Back to article
[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.102
Search in Google Scholar Back to article
[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. 2006.10.1016/j.renene.2005.07.006
Search in Google Scholar Back to article
[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.2010010104
Search in Google Scholar Back to article
[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. 577-580.10.1109/ISCAS.2001.921376
Search in Google Scholar Back to article
[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/s00202-005-0303-5
Search in Google Scholar Back to article
[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.2010.03.001
Search in Google Scholar Back to article
[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 Back to article
[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.2016.7727775
Search in Google Scholar Back to article
[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.36-41.10.1109/CNNA.1992.274330
Search in Google Scholar Back to article
[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/s00500-006-0075-5
Search in Google Scholar Back to article
[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.1979.1084659
Search in Google Scholar Back to article
[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/BF01068667
Search in Google Scholar Back to article
[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_55
Search in Google Scholar Back to article
[15] I. Aizenberg, I., Complex-Valued Neural Networks with Multi-Valued Neurons. Berlin: Springer-Verlag Publishers, 2011.10.1007/978-3-642-20353-4
Search in Google Scholar Back to article
[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.2007.91415818467216
Search in Google Scholar Back to article
[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/s00500-011-0755-7
Search in Google Scholar Back to article
[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.ch8
Search in Google Scholar Back to article
[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/s00500-012-0891-8
Search in Google Scholar Back to article
[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 Back to article
[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. 1998.10.1109/19.744205
Search in Google Scholar Back to article
[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, 1994.10.1109/IMTC.1994.352094
Search in Google Scholar Back to article
[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. 1999.10.1109/81.774222
Search in Google Scholar Back to article
[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.2003.809811
Search in Google Scholar Back to article
[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, 2016.10.1002/cta.2164
Search in Google Scholar Back to article
[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.2016.26450792017
Open DOI Search in Google Scholar Back to article
[27] R. Berkowitz, Conditions for network-elementvalue solvability, IRE Trans. Circ. Theory, vol. 9, pp. 24-29, 1962.10.1109/TCT.1962.1086882
Search in Google Scholar Back to article
[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 Back to article
[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.1981.1675719
Search in Google Scholar Back to article
[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.1983.1103302
Search in Google Scholar Back to article
[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.1987.6312710
Search in Google Scholar Back to article
[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, 1989.10.1109/19.39034
Search in Google Scholar Back to article
[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;2-6
Search in Google Scholar Back to article
[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. 945-948.10.1109/ISCAS.1988.15079
Search in Google Scholar Back to article
[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. 992-995.10.1109/ISCAS.2005.1464757
Search in Google Scholar Back to article
[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.21671
Search in Google Scholar Back to article
[37] W. S. Bennett, Properly Applied Antenna Factors, IEEE Trans. on Electromagnetic Compatibility, EMC-28, 1, pp. 2-6, Feb. 1986.10.1109/TEMC.1986.4307232
Search in Google Scholar Back to article
[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, 2002.10.1080/02726340290083923
Search in Google Scholar Back to article
[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 Back to article
[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.2009.2026423
Search in Google Scholar Back to article
[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, 2009.10.1108/03321640610634407
Search in Google Scholar Back to article

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

Abstract

Paradigm

My account