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ANN Implementation of Constructing Logic Gates Focusing On Ex-NOR

Author Affiliations

  • 1Dept. of Computer Science and Engineering, Institute of Technology, Guru GhasidasVishwavidyalaya, Central University, Bilaspur, CG, India

Res. J. Computer & IT Sci., Volume 4, Issue (6), Pages 1-11, June,20 (2016)

Abstract

In this paper Construction of Logic Gates using Artificial Neural Network is discussed. The solution to the problem of Construction of Logic gates is discussed. The proof of the solution proposed is provided. The Artificial Neural Network utilized for providing the solution to the problem of construction of logic gates uses fixed set of weights to generate the output. The Artificial Neural Network model follows single layer network topology. Although there are two layers since computation it is performed only in one layer and one neuron it is single layer network. In this paper a new solution to the Ex-NOR problem is provided

References

  1. McCulloch W.S. and Pitts W. (1943)., A logical calculus of the ideas immanent in nervous activity., Bull. Math. Biophy., 5, 115-133.
  2. Havati M. and Mohebi Z (2007)., Application of Artificial Neural Networks for Temperature Forecasting., International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, 1(4), 28.
  3. Grossberg S. (1976)., Adaptive Pattern Classification and Universal Recoding Parallel Development and Coding of Neural Feature Detectors., Biological Cybernetics, 23, 121-134.
  4. Bevilacqua M. (2005)., Failure Rate Prediction with Artificial Neural Networks., Journal of Quality in Maintenance Engineering, Emerald Group Publishing Limited, 11(3), 279-294.
  5. Radhika Y. and Shashi M. (2009)., Atmospheric Temperature Prediction using Support Vector Machines., International Journal of Computer Theory and Engineering, 1(1), 1, 1793-8201.
  6. Gowri T.M. and Reddy V.V.C. (2008)., Load Forecasting by Novel Technique using ANN., Journal of Engineering and Applied Science, 3(2), 19-25.
  7. Yegna Narayana B. (2012)., Artificial Neural Network., PHI, ISBN: 978-81-203-1253-1.
  8. Haykin S. (1999)., Neural Networks a Comprehensive Foundation., Tsinghua University Press, PHI, ISBN: 0-13-908385-5.
  9. Das R.P. and Sreedhar L. (2012)., Neural Network and Fuzzy Logic., S.K. Kataria and Sons, ISBN: 978-93-5014-270-7.
  10. Rich E. and Knight K. (2009)., Artificial Intelligence., TMH.
  11. Singh V.K. (2015)., One Solution to XOR Problem using Multilayer Perceptron having Minimum Configuration., International Journal of Science and Engineering, 3(3), 32-41.
  12. Singh V.K. (2015)., Two Solution to the XOR Problem using Minimum Configuration MLP., International Journal of Adavance Engineering Science and Technological Research, 3(3), 16-20.
  13. Singh V.K. (2016)., Proposing Solution to XOR Problem using Minimum Configuration MLP., Published in Procedia Computer Science Elsevier, Science Direct, International Conference on Computational Modeling and Security, CMS 2016, Bangalore, India, 11-13 Feb, 255-262.
  14. Singh V.K. (2016)., Minimum Configuration MLP for solving the XOR problem., Published in Proceeding of IEEE International Conference on Computing for Sustainable Global Decelopment, INDIACom-2016, Delhi, India, IEEE Conference ID:37465, ISSN:0973-7529, ISBN: 978-93-80544-20-5, March, pp 168-173.
  15. Singh V.K. (2016)., Mathematical Explanation to Solution for Ex-NOR problem using MLFFN., International Journal of Information Sciences and Techniques, 6(1/2), 105-122.