Weight assignment algorithms for designing fully connected neural network
Автор: Aarti M. Karande, D. R. Kalbande
Журнал: International Journal of Intelligent Systems and Applications @ijisa
Статья в выпуске: 6 vol.10, 2018 года.
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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.
Soft Computing, Neural Network, Saaty’s Method, Analytical Hierarchical Processing, Exact Linear Algebra Calculation, Geometric Average Approximation, Successive Matrix Squaring
Короткий адрес: https://sciup.org/15016501
IDR: 15016501 | DOI: 10.5815/ijisa.2018.06.08
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