scholarly journals Construction of Self-Organizing Fuzzy Controllers by Neural Networks

1990 ◽  
Vol 26 (8) ◽  
pp. 862-869
Author(s):  
Toshiyuki TAKAGI ◽  
Shohachiro NAKANISHI ◽  
Keizo UNEHARA ◽  
Yasuyuki GOTOH
Keyword(s):  
Neural Networks ◽  
Fuzzy Controllers ◽  
Construction Of Self ◽  
Self Organizing

Design and analysis of a class of self-organizing and trainable fuzzy controllers

1995 ◽  
Vol 12 (3) ◽  
pp. 301-315 ◽  
Author(s):  
Taek Mu Kwon ◽  
Michael E. Zervakis ◽  
Anastasios N. Venetsanopoulos
Keyword(s):  
Fuzzy Controllers ◽  
Self Organizing

scholarly journals Spatial and Temporal Spread of the COVID-19 Pandemic Using Self Organizing Neural Networks and a Fuzzy Fractal Approach

2021 ◽  
Vol 13 (15) ◽  
pp. 8295
Author(s):  
Patricia Melin ◽  
Oscar Castillo
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Temporal Trends ◽  
Main Idea ◽  
Temporal Analysis ◽  
Virus Propagation ◽  
Self Organizing Maps ◽  
Fractal Approach ◽  
The Times ◽  
Self Organizing

In this article, the evolution in both space and time of the COVID-19 pandemic is studied by utilizing a neural network with a self-organizing nature for the spatial analysis of data, and a fuzzy fractal method for capturing the temporal trends of the time series of the countries considered in this study. Self-organizing neural networks possess the capability to cluster countries in the space domain based on their similar characteristics, with respect to their COVID-19 cases. This form enables the finding of countries that have a similar behavior, and thus can benefit from utilizing the same methods in fighting the virus propagation. In order to validate the approach, publicly available datasets of COVID-19 cases worldwide have been used. In addition, a fuzzy fractal approach is utilized for the temporal analysis of the time series of the countries considered in this study. Then, a hybrid combination, using fuzzy rules, of both the self-organizing maps and the fuzzy fractal approach is proposed for efficient coronavirus disease 2019 (COVID-19) forecasting of the countries. Relevant conclusions have emerged from this study that may be of great help in putting forward the best possible strategies in fighting the virus pandemic. Many of the existing works concerned with COVID-19 look at the problem mostly from a temporal viewpoint, which is of course relevant, but we strongly believe that the combination of both aspects of the problem is relevant for improving the forecasting ability. The main idea of this article is combining neural networks with a self-organizing nature for clustering countries with a high similarity and the fuzzy fractal approach for being able to forecast the times series. Simulation results of COVID-19 data from countries around the world show the ability of the proposed approach to first spatially cluster the countries and then to accurately predict in time the COVID-19 data for different countries with a fuzzy fractal approach.

scholarly journals Self-organizing Neural Networks for Modeling Robust 3D and 4D QSAR: Application to Dihydrofolate Reductase Inhibitors

Molecules ◽  
2004 ◽  
Vol 9 (12) ◽  
pp. 1148-1159 ◽  
Author(s):  
Jaroslaw Polanski ◽  
Andrzej Bak ◽  
Rafal Gieleciak ◽  
Tomasz Magdziarz
Keyword(s):  
Neural Networks ◽  
Dihydrofolate Reductase ◽  
Reductase Inhibitors ◽  
Dihydrofolate Reductase Inhibitors ◽  
Self Organizing
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