Weight Assignment Algorithms for Designing Fully Connected Neural Network

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Aarti M. Karande 1,* D. R. Kalbande 2

1. Computer Engineering, S.P.I.T. Mumbai

2. Dean (Industry Relation), HOD Computer Engineering, S.P.I.T. Mumbai

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2018.06.08

Received: 24 May 2017 / Revised: 2 Aug. 2017 / Accepted: 15 Sep. 2017 / Published: 8 Jun. 2018

Index Terms

Soft Computing, Neural Network, Saaty’s Method, Analytical Hierarchical Processing, Exact Linear Algebra Calculation, Geometric Average Approximation, Successive Matrix Squaring


Soft computing is used to solve the problems where input data is incomplete or imprecise. This paper demonstrate designing fully connected neural network system using four different weight calculation algorithms. Input data for weight calculation is constructed in the matrix format based on the pairwise comparison of input constraints. This comparison is performed using saaty’s method. This input matrix helps to build judgment between several individuals, forming a single judgment. Algorithm considered here are Geometric average mean, Linear algebra calculation, Successive matrix squaring method, and analytical hierarchical processing method. Based on the quality parameter of performance, it is observed that analytical hierarchical processing is the most promising mathematical method for finding appropriate weight. Analytical hierarchical processing works on structuration of the problem into sub problems, Hence it the most prominent method for weight calculation in fully connected NN.

Cite This Paper

Aarti M. Karande, D. R. Kalbande, "Weight Assignment Algorithms for Designing Fully Connected Neural Network", International Journal of Intelligent Systems and Applications(IJISA), Vol.10, No.6, pp.68-76, 2018. DOI:10.5815/ijisa.2018.06.08


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