Advertisement

Hardware Implementation of an Analog Neural Nonderivative Optimizer

  • Rodrigo Cardim
  • Marcelo C. M. Teixeira
  • Edvaldo Assunção
  • Nobuo Oki
  • Aparecido A. de Carvalho
  • Márcio R. Covacic
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)

Abstract

Analog neural systems that can automatically find the minimum value of the outputs of unknown analog systems, described by convex functions, are studied. When information about derivative or gradient are not used, these systems are called analog nonderivative optimizers. An electronic circuit for the analog neural nonderivative optimizer proposed by Teixeira and Żak, and its simulation with software PSPICE, is presented. With the simulation results and hardware implementation of the system, the validity of the proposed optimizer can be verified. These results are original, from the best of the authors knowledge.

Keywords

Hardware Implementation Disturbance Rejection SCHMITT Trigger Power Factor Correction Input Waveform 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Korovin, S., Utkin, V.I.: Using sliding modes in static optimization and nonlinear programming. Automatica 10, 525–532 (1974)CrossRefMATHMathSciNetGoogle Scholar
  2. 2.
    Teixeira, M.C.M., Żak, S.H.: Analog nonderivative optimizers. In: American Control Conference - ACC, Albuquerque, New, Mexico, USA, pp. 3592–3596 (1997)Google Scholar
  3. 3.
    Teixeira, M.C.M., Żak, S.H.: Analog neural nonderivative optimizers. IEEE Transactions on Neural Networks 9(4), 629–638 (1998)CrossRefGoogle Scholar
  4. 4.
    Stout, D.F.: Handbook of Operational Amplifier Circuit Design. M. Kaufman, McGraw-Hill, New York (1976)Google Scholar
  5. 5.
    Cichocki, A., Unbehauen, R.: Neural Networks for Optimization and Signal Processing. John Wiley, Chichester (1993)MATHGoogle Scholar
  6. 6.
    Will, A.B.: Intelligent Vehicle Steering and Braking Control Systems. PhD thesis, School of Electrical Engineering, Purdue University, West Lafayette, IN (1997)Google Scholar
  7. 7.
    Żak, S.H., Will, A.B.: Sliding mode wheel slip controller for an antilock braking system. International Journal of Vehicle 19, 523–539 (1998)Google Scholar
  8. 8.
    Czernichovski, S.: Otimizadores analógicos não derivativos. Msc thesis, UNESP - São Paulo State University, Ilha Solteira - SP, Brazil (2001)Google Scholar
  9. 9.
    Lee, Y., Żak, S.H.: Genetic neural fuzzy control of anti-lock brake systems. In: American Control Conference - ACC, Arlington, Virginia, USA, pp. 671–676 (2001)Google Scholar
  10. 10.
    Lee, Y., Żak, S.H.: Designing a genetic neural fuzzy antilock-brake-system controller. IEEE Transactions on Evolutionary Computation 6(2), 198–211 (2002)CrossRefGoogle Scholar
  11. 11.
    Cardim, R., Teixeira, M.C.M., Assunção, E.: Utilização de um otimizador analógico não-derivativo para a correção do fator de potência. In: II Congresso Temático de Dinâmica e Controle da SBMAC, São José dos Campos - SP, Brazil, pp. 1474–1483 (2003)Google Scholar
  12. 12.
    Sedra, A.S., Smith, K.C.: Microeletrônica. Makron Books Ltd, Brazil (2000)Google Scholar
  13. 13.
    Franco, S.: Design Operational Amplifiers and Analog Integrated Circuits. McGraw-Hill, New York (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Rodrigo Cardim
    • 1
  • Marcelo C. M. Teixeira
    • 1
  • Edvaldo Assunção
    • 1
  • Nobuo Oki
    • 1
  • Aparecido A. de Carvalho
    • 1
  • Márcio R. Covacic
    • 1
  1. 1.Department of Electrical EngineeringUNESP – São Paulo State UniversityIlha Solteira, São PauloBrazil

Personalised recommendations