ANN Approach for Error Optimization in Process Synchronization Using Back-Propagation Algorithm

Authors(2) :-Sayma Bano, Neelendra Badal

The work includes in this paper is inspired from the convolution involved for solving the problems by a neural network. The aspiration of this paper is to demonstrate the minimization of error for problem-solving techniques, like process synchronization, Classification etc. The work includes three Artificial Neural Network Algorithms i.e., Feed-forward, Back-propagation and Output-Hidden Weight Optimization. Optimization is being achieved with the minimized error rate for the problem-solving using neural system approach.

Authors and Affiliations

Sayma Bano
Department of Computer Science & Engineering, Kamla Nehru Institute of Technology, Sultanpur, Uttar Pradesh, India
Neelendra Badal
Department of Computer Science & Engineering, Kamla Nehru Institute of Technology, Sultanpur, Uttar Pradesh, India

Back-propagation Error Methodology, Optimization, Synchronization

  1. I., Abu-Mahfouz, "An Absolute Study of Three Artificial Neural Networks for the Detection and Classification", In International Journal of General Systems, vol.34 (3), pp. 261-77, 2005.
  2. M.,Alsmadi, K., Omar, and S., Noah, "Back-Propagation Algorithm: The Best Algorithm among the Multi-layer Perceptron Algorithm", in International Journal of Computer Science and Network Security, vol.9 (4), pp.378-83, 2009.
  3. P., Atkinson, and A., Tatnall, "Introduction Neural Networks in Remote Sensing", In International Journal of Remote Sensing, vol.18 (4), pp.699-709, 1997.
  4. H.H, Chen, M.T, Manry, H., Chandrasekaran, "A Neural Network Algorithm Developing multiple Sets of Linear Equations", Neuro-computing, vol.25 (1-3), pp.55-72, 1999.
  5. J.J, Hopfield, "Neural Networks and Physical System with emergent collective computational abilities", In Proceedings of National Academy of Sciences of the USA, vol.79 (8), pp.2554-2558, 1982.
  6. T., Jayalakshmi, and A., Santhakumaran,"Improved Gradient Descent Back-Propagation Neural Networks for Diagnoses of Type II Diabetes Mellitus", In Global Journal of Computer Science and Technology, vol.9, pp.94-97, 2010.
  7. A.K., Jain, J., Mao, and K.M., Mohiuddin,"Artificial Neural Networks: A Tutorial", Computer, vol.29 (3), pp.31-44, 1996.
  8. L.K., Li, S., Shao, and K.F., Yiu,"A New Optimization Algorithm for single Hidden layer Feed-forward Neural Network", applied In Soft Computing, vol.13 (5), pp.2857-62, 2013.
  9. W.S., McCulloch, and W., Pitts,"A Logical Calculus of ideas immanent in neurons activity", Bulletin of Mathematical Biophysics, vol.5, pp.115-133, 1943.
  10. D., Nguyen, and B., Windrow, "Improving the Learning Speed of 2-layer Neural Networks by choosing Initial Values of the Adaptive Weights", Proceedings of the IEEE International Joint Conference on Neural Networks, vol.3, pp.21-26, 1990. 
  11. G., Nicolette, "An Analysis of Neural Networks as Simulators and Emulators", Cybernetics and Systems, vol.31 (3), pp.253-82, 2000.
  12. J., Richards,"Remote Sensing Digital Image Analysis", Berlin: Springer-Verlag, 2006.
  13. F., Rosenblatt, "The Perceptron: a probabilistic model for information storage and organization in the brain", Psychological Review, vol.65, pp.386-408, 1958.
  14. D.E., Rumelhart, G.E., Hinton, and R.J., Williams, "Learning Representations by Back-propagating Errors nature", vol.323, pp.533-536, 1986.
  15. M., Sheikhan, A.A., Sha’bani, "PSO-Optimized Modular Neural Network trained by OWO-HWO Algorithm for Fault Location in Analog Circuits", in Neural Computation Application, vol.23 (2), pp.519-530, 2013.
  16. T.S., Wang, L., Chen, C.H., Tan, H.C., Yeh, and Y.C., Tsai, "BPN for Land Cover Classification by using Remotely Sensed Data", In Proceedings of Fifth International Conference On Natural Computation, IEEE, pp.535-9, 2009.
  17. A., Wanto, A.P., Windarto, D., Hartman, and I., Parlina, "Use of Binary Sigmoid Function And Linear Identity In Artificial Neural Networks For Forecasting Population Density", In International Journal of Information System and Technology, vol.1 (1), pp.43-54, 2017.
  18. P., Werbos, "Generalization of Backpropagation with Application to a recurrent gas market model", Neural Networks, Stand ford University, vol.1, pp.339-356, 1988.

Publication Details

Published in : Volume 3 | Issue 5 | May-June 2018
Date of Publication : 2018-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 477-485
Manuscript Number : CSEIT183594
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Sayma Bano, Neelendra Badal , "ANN Approach for Error Optimization in Process Synchronization Using Back-Propagation Algorithm", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 5, pp.477-485, May-June-2018.
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