Warsono, Warsono and Alfa D, Gerry and Kurniasari, Dian and Usman, Mustofa (2016) Neural Network Fuzzy Learning Vector Quantization (FLVQ) to Identify Probability Distributions. IJCSNS International Journal of Computer Science and Network Security, 16 (10). pp. 16-19. ISSN 1738-7906

Preview

Text WARSONO IJCSNS.pdf Download (190kB) | Preview

Abstract

A Statistical model is built based on a probability distribution. Classically, probability distribution is identified by some methods for example by using Chi-square goodness of fits, by using graph, by nonparametric goodness of fits test, and by using normal plot to test the normality. The aim of this study is going to discuss the applications of Fuzzy Learning Vector Quantization (FLVQ) model to identify some probability distributions; this model is a merger between neural network and fuzzy set. The results from the application of this FLVQ through a simulation to identify the probability distributions are very good and can be implemented to a real data.

Item Type:	Article
Subjects:	Q Science > QA Mathematics
Divisions:	Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA) > Prodi Magister Ilmu Matematika
Depositing User:	MUSTOFA US
Date Deposited:	11 Nov 2016 02:36
Last Modified:	11 Nov 2016 02:36
URI:	http://repository.lppm.unila.ac.id/id/eprint/1137

Actions (login required)

View Item