Title: To solve multi-class pattern classification problems by grid neural network

Authors: Ajendra Kumar; Preet Pal Singh; Dipa Sharma; Pawan Joshi

Addresses: Department of Mathematics and Statistics, Gurukula Kangri (deemed to be university), Haridwar, Uttrakhand, 249404, India ' Department of Mathematics, S.D.M Govt (P.G) College Doiwala, Dehradun, Uttrakhand, 248140, India ' Department of Mathematics, P.T.L.M.S. (P.G) College, Rishikesh, Uttrakhand, 249201, India ' Department of Applied Sciences, Tula's Institute, Dhoolkot, Dehradun, Uttrakhand, 248007, India

Abstract: Grid computing is employed to unravel massive computational problems by using large numbers of heterogeneous computers connected to the computing network. Job scheduling is an important part of the grid computing environment, which is employed to extend the throughput and reduce the turnaround and reaction time. This paper proposed a new scheduling algorithm called "Feed forward neural network in the grid computing (FFNNGC) system," which is used to solve some real-life problems related to the pattern classification. In the proposed method, we have used a feed-forward algorithm to find the output in the grid computing network, and the network training is done until the system converges to a minimum error solution. The pattern classification problem consists of 13 real-life, and artificial dataset problems, including two class and multiclass problems. Experiments were performed under these real-life problems, and the results indicated that the proposed method is helpful in such types of problems.

Keywords: grid computing; pattern classification; artificial neural network; distributed heterogeneous systems; feed forward algorithm; back propagation algorithm; FFNN; feed forward neural networks.

DOI: 10.1504/IJCSM.2022.124003

International Journal of Computing Science and Mathematics, 2022 Vol.15 No.2, pp.183 - 197

Received: 06 Jul 2020
Accepted: 04 Jan 2021
Published online: 07 Jul 2022 *

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