Artificial Neural Network Model Adopting Combinatorial Inhibition Process in Multiple Solution Problems

  • Authors

    • K Shyamala
    • P Chanthini
    • R Krishnan
    • A Murugan
    2018-06-25
    https://doi.org/10.14419/ijet.v7i3.4.16767
  • Combinatorics, Inhibition, Non-Modular neural network, Selection, Sequential Firing

  • Exploration of Artificial Neural Network (ANN) research continually opens rooms for improvement and implementation of mathematical models to solve various problems. This research work was not only to direct on the objective of problem-solving, instead the goal is to mimic basic biological functions of the brain in problem-solving situations. The basic biological theories of “Selectionâ€, “Combination†and “Inhibition†were successfully implemented in the earlier works. This work conceived another biological theory named “Sequential Firing†of neuron in solving complex problems like sum-of-subset problems. The non-modular combinatorial inhibition neural model has been proposed and implemented successfully using the time delayed sequential firing between neurons. As per the biological theories knowledge representation is a preliminary phase of learning. This work not only illustrates the sequential process of firing between neurons, it paves the way to utilize this neural model for the learning process.

     

  • References

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      Shyamala, K., Chanthini,P., Krishnan,R., and A. Murugan. “Adoption of combinatorial graph for inhibitory process in optimization problems.†International Journal of Applied Engineering Research (IJAER) (2018)(“in-pressâ€).

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  • How to Cite

    Shyamala, K., Chanthini, P., Krishnan, R., & Murugan, A. (2018). Artificial Neural Network Model Adopting Combinatorial Inhibition Process in Multiple Solution Problems. International Journal of Engineering & Technology, 7(3.4), 167-173. https://doi.org/10.14419/ijet.v7i3.4.16767