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

[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. 2006.10.1016/j.renene.2005.07.006Search 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, 2016.10.1002/cta.2164Search 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