Generalised Neural Network

Authors

  • Sunil Kumar Kashyap School of Advanced Sciences, Vellore Institute of Technology University, Vellore, Tamil Nadu
  • Deepshikha Sharma Department of Computer Science, Kalinga University, Raipur, Chhattisgarh

DOI:

https://doi.org/10.37628/ijece.v3i2.697

Abstract

The neural network is generalised in this paper. The resultant becomes more feasible than earlier. The applicability of the proposed is discussed in this paper also. The new characteristics found in the neural network. The probabilistic study based neural network is performed in this paper. The composition of discrete and continuous probability performed for generalising the neural network in this paper. An efficient neural network performance is presented in this paper by using this composition. The computational complexity lies with this composition provides the security at high level. The feasibility of the neural network comprises with the optimized computational complexity.

Author Biographies

Sunil Kumar Kashyap, School of Advanced Sciences, Vellore Institute of Technology University, Vellore, Tamil Nadu

Department of Mathematics

Deepshikha Sharma, Department of Computer Science, Kalinga University, Raipur, Chhattisgarh

Department of Computer Science

References

G.V. Cybenko. Approximation by Superpositions of a Sigmoidal function, Math Control Signals Syst. 1989; 2: 303–14p.

H.K.D.H. Bhadeshia. neural networks in materials science (PDF), ISIJ Int. 1999; 39(10): 966–79p.

S. Lyons. Changes in cell shape are correlated with metastatic potential in murine, Biol Open. 2016; 5(3): 289–9p.

A. Einstein. Uber die yom relativitatsprinzip geforderte tragheit der Energie, Ann Phys. 328(7): 371–84p.

G.S. Ohm. Die galvanische Kette: Mathematisch Bearbeitet. Reimann, 181, 1827.

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Published

2018-03-31

Issue

Vol. 3 No. 2 (2017)

Section

Articles