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Method of Measurement of Capacitance and Dielectric Loss Factor Using Artificial Neural Networks Cover

Method of Measurement of Capacitance and Dielectric Loss Factor Using Artificial Neural Networks

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Open Access
|Jul 2015

References

  1. [1] Cichy, A., Skorkowski, A., Barwinek, S. (2013). Automated quasi-balancing in virtual quasi-balanced circuit designed to capacitance measurements. In, 18-19 July 2013, Barcelona, Spain, 275-280.
  2. [2] Skorkowski, A., Cichy, A. (2009). Virtual capacitance meter based on impedance modulus measurement. In, 6-11 September 2009, Lisbon, Portugal, 648-651.
  3. [3] Amira, H., Hfaiedh, M., Valentin, M. (2009). Quasi-balanced bridge method for the measurements of the impedances., 3 (6), 403-409.
  4. [4] Atmanand, M.A., Jagadeesh Kumar, V., Murti, V.G.K. (1996). A microcontroller based quasi-balanced bridge for the measurement of L, C and R., 45 (3), 1-5.
  5. [5] Burbelo, M.I. (2001). Universal quasi-balanced bridges for measuring the parameters of four-element two-terminal networks., 44 (11), 1130-1133.
  6. [6] Atmanand, M.A., Jagadeesh Kumar, V. (1996). A microcontroller based LCR meter., 20, 297-301.
  7. [7] Atmanand, M.A., Jagadeesh Kumar, V., Murti, V.G.K. (1995). A novel method of measurement of L and C., 44 (4), 898-903.
  8. [8] Marcuta, C., Fosalau, C., Petrescu, C. (2005). A virtual impedance measuring instruments based on quasi-balanced bridge. In, 12-18 September 2005, Gdynia Maritime University, Poland, Vol. III, 517-523.
  9. [9] Jagadeesh Kumar, V., Sankaran, P., Sudhakar Rao, K. (2003). Measurement of C and tan δ of a capacitor employing PSDs and dual slope DVMs., 52, (5), 1588-1592.
  10. [10] Cichy, A. (2013). Non-bridge circuit with double quasi-balancing for measurement of dielectric loss factor.nology, 7 (5), 274-279.
  11. [11] Roj, J. (2013). Neural approximation of empirical functions., 124 (3), 554-557.
  12. [12] Hornik, K., Stinchcombe, M., White, H. (1989). Multilayer feedforward networks are universal approximators., 2, 359-366.
  13. [13] Hornik, K. (1991). Approximation capabilities of multilayer feedforward networks., 4 (2), 251-257.
  14. [14] Haykin, S. (2008).. Third Edition. Prentice Hall.
  15. [15] Gupta, M., Homma, N., Jin, L. (2003). John Wiley & Sons.
  16. [16] Beale, M.H., Hagan, M.T., Demuth, H.B. (2015).MathWorks Inc.
  17. [17] Hagan, M.T., Menhaj, M. (1994). Training feedforward networks with the Marquardt algorithm., 5 (6), 989-993.
  18. [18] Glowacz, A., Glowacz, A., Glowacz, Z. (2014). Recognition of monochrome thermal images of synchronous motor with the application of quadtree decomposition and backpropagation neural network.(), 16 (1), 92-96.
  19. [19] Roj, J. (2014). Estimation of the artificial neural network uncertainty used for measurand reconstruction in a sampling transducer., 8 (1), 23-29.
Language: English
Page range: 127 - 131
Submitted on: Sep 2, 2014
Accepted on: Jun 24, 2015
Published on: Jul 10, 2015
Published by: Slovak Academy of Sciences, Institute of Measurement Science
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open

© 2015 Jerzy Roj, Adam Cichy, published by Slovak Academy of Sciences, Institute of Measurement Science
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.