Industrial Applications of Soft Computing

  • Marco Branciforte
  • Riccardo Caponetto
  • Mario Lavorgna
  • Luigi Occhipinti
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 11)


In this paper two industrial applications of soft computing methodologies, developed by STMicroelectronics, are described. The main idea, in designing dedicated ICs based on soft computing paradigm, is to produce competitive devices characterized by high MIQ (Machine Intelligence Quotient).


Soft Computing Cellular Neural Network Ultrasonic Sensor Turing Pattern Adaptable Fuzzy Controller 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    L. A. Zadeh, “Fuzzy logic, neural network and soft computing”, Commun. ACM, vol 37, pp 77–84, March 1994.CrossRefGoogle Scholar
  2. 2.
    P. Arena, L. Fortuna, M. Branciforte, “Reaction-Diffusion CNN Algorithms to Generate and Control Artificial Locomotion”, IEEE Transaction on Circuits and Systems, I: Fundam Theory and Applic, Vol. 46 N2, pp. 259–266, Feb. 1999Google Scholar
  3. 3.
    R. Caponetto, G. Manganaro, L.Fortuna, M.G. Xibilia, “Chaotic System Identification via Genetic Algorithms”, 1st IEE/IEEE International Conference GALESIA, 12–14 September Sheffield UK, 1995.Google Scholar
  4. 4.
    R. Caponetto, M. Criscione, L. Fortuna, D. Occhipinti, L. Occhipinti, “Synthesis of a Programmable Chaos Generator, based on CNN Architectures, with Applications in Chaotic Communication”, CNNA ‘88, London UK, 14–17 April 1998.Google Scholar
  5. 5.
    R. Caponetto, M. Lavorgna, A. Martinez, L. Occhipinti, “Cellular Neural Network Simulator for Image Processing”, CNNA ‘88, London UK, 14–17 April 1998.Google Scholar
  6. 6.
    M. Lavorgna, L. Occhipinti, R. Caponetto, L. Fortuna, G. Di Bernardo, “Cellular Fuzzy Processor: New Architecture to explore Complexity in Locally Interconnected System”, IEEE Int. Conf. On Electronics Circuits and Systems Lisbon 7–10 Sept. 1998.Google Scholar
  7. 7.
    L. O. Chua, M. Hasler, G. S. Moschytz, J. Neirynck, “Autonomous Cellular Neural Networks: A Unified Paradigm for Pattern Formation and Active Wave Propagation”, IEEE Trans. on Circuits and Systems- Part I, vol. 42 no. 10, pp. 559–577, 1995.MathSciNetCrossRefGoogle Scholar
  8. 8.
    D. E. Rumelhart, McClelland, “Parallel Distributed Processing: Exploration in the microstructure of cognition”, pp.318–362, MIT Press, 1986.Google Scholar
  9. 9.
    G. M. Shepherd, “Neurobiology”, Oxford Univ. Press, third edition, 1994.Google Scholar
  10. 10.
    P. Arena, L. Fortuna, M. Branciforte, “ Reaction-Diffusion CNN algorithms to generate and control artificial locomotion” IEEE Trans. on Circuits and Systems in press.Google Scholar
  11. 11.
    P. Arena, L. Fortuna, and G. Manganaro, “Self organization in a two layer CNN.” IEEE Trans. Circuits Syst. I, Vol. 45, pp. 157–162, 1998CrossRefGoogle Scholar
  12. 12.
    P.Arena, L. Fortuna, and G. Manganaro, “A CNN for pattern formation and active wave propagation.” in Proc. European Conf. Circuit Theory Design, ECCTD ‘87, Budapest, Ungary, Aug. 1997Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Marco Branciforte
    • 1
  • Riccardo Caponetto
    • 1
  • Mario Lavorgna
    • 1
  • Luigi Occhipinti
    • 1
  1. 1.STMicroelectronicsSoft Computing GroupCataniaItaly

Personalised recommendations