scholarly journals Neural Network Pattern Classification and Weather Dependent Fuzzy Logic Model for Irrigation Control in WSN Based Precision Agriculture

2016 ◽  
Vol 78 ◽  
pp. 499-506 ◽  
Author(s):  
Ambarish G. Mohapatra ◽  
Saroj Kumar Lenka
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Pattern Classification ◽  
Precision Agriculture ◽  
Logic Model ◽  
Fuzzy Logic Model ◽  
Irrigation Control ◽  
Network Pattern

The Prediction of Needle Penetration Force in Woven Denim Fabrics Using Soft Computing Models

2014 ◽  
Vol 9 (4) ◽  
pp. 155892501400900 ◽  
Author(s):  
Ezzatollah Haghighat ◽  
Saeed Shaikhzadeh Najar ◽  
Seyed Mohammad Etrati
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Soft Computing ◽  
Logic Model ◽  
Needle Penetration ◽  
Fuzzy Logic Model ◽  
Penetration Force ◽  
Artificial Neural ◽  
Computing Models

The aim of this paper was to predict the needle penetration force in denim fabrics based on sewing parameters by using the fuzzy logic (FL) model. Moreover, the performance of fuzzy logic model is compared with that of the artificial neural network (ANN) model. The needle penetration force was measured on the Instron tensile tester. In order to plan the fuzzy logic model, the sewing needle size, number of fabric layers and fabric weight were taken into account as input parameters. The output parameter is needle penetration force. In addition, the same parameters and data are used in artificial neural network model. The results indicate that the needle penetration force can be predicted in terms of sewing parameters by using the fuzzy logic model. The difference between performance of fuzzy logic and neural network models is not meaningful ( RFL=0.971 and RANN=0.982). It is concluded that soft computing models such as fuzzy logic and artificial neural network can be utilized to forecast the needle penetration force in denim fabrics. Using the fuzzy logic model for predicting the needle penetration force in denim fabrics can help the garment manufacturer to acquire better knowledge about the sewing process. As a result, the sewing process may be improved, and also the quality of denim apparel increased.

scholarly journals Modeling Dark Fermentation of Coffee Mucilage Wastes for Hydrogen Production: Artificial Neural Network Model vs. Fuzzy Logic Model

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1663
Author(s):  
Edilson León Moreno Cárdenas ◽  
Arley David Zapata-Zapata ◽  
Daehwan Kim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Hydrogen Production ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Logic Model ◽  
Fuzzy Logic Model ◽  
Artificial Neural

This study presents the analysis and estimation of the hydrogen production from coffee mucilage mixed with organic wastes by dark anaerobic fermentation in a co-digestion system using an artificial neural network and fuzzy logic model. Different ratios of organic wastes (vegetal and fruit garbage) were added and combined with coffee mucilage, which led to an increase of the total hydrogen yield by providing proper sources of carbon, nitrogen, mineral, and other nutrients. A two-level factorial experiment was designed and conducted with independent variables of mucilage/organic wastes ratio, chemical oxygen demand (COD), acidification time, pH, and temperature in a 20-L bioreactor in order to demonstrate the predictive capability of two analytical modeling approaches. An artificial neural network configuration of three layers with 5-10-1 neurons was developed. The trapezoidal fuzzy functions and an inference system in the IF-THEN format were applied for the fuzzy logic model. The quality fit between experimental hydrogen productions and analytical predictions exhibited a predictive performance on the accumulative hydrogen yield with the correlation coefficient (R2) for the artificial neural network (> 0.7866) and fuzzy logic model (> 0.8485), respectively. Further tests of anaerobic dark fermentation with predefined factors at given experimental conditions showed that fuzzy logic model predictions had a higher quality of fit (R2 > 0.9508) than those from the artificial neural network model (R2 > 0.8369). The findings of this study confirm that coffee mucilage is a potential resource as the renewable energy carrier, and the fuzzy-logic-based model is able to predict hydrogen production with a satisfactory correlation coefficient, which is more sensitive than the predictive capacity of the artificial neural network model.

A Fuzzy Logic Model to Predict Coral Reef Development under Nutrient and Sediment Stress

1998 ◽  
Vol 12 (5) ◽  
pp. 957-965 ◽  
Author(s):  
Erik H. Meesters ◽  
Rolf P. M. Bak ◽  
Susie Westmacott ◽  
Mark Ridgley ◽  
Steve Dollar
Keyword(s):  
Fuzzy Logic ◽  
Coral Reef ◽  
Logic Model ◽  
Reef Development ◽  
Fuzzy Logic Model

scholarly journals A Fuzzy Logic Model for Hourly Electrical Power Demand Modeling

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 448
Author(s):  
Marco Antonio Islas ◽  
José de Jesús Rubio ◽  
Samantha Muñiz ◽  
Genaro Ochoa ◽  
Jaime Pacheco ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
New England ◽  
Electrical Power ◽  
Logic Model ◽  
Power Demand ◽  
Demand Modeling ◽  
Mapping Model ◽  
Fuzzy Logic Model ◽  
Radial Basis

In this article, a fuzzy logic model is proposed for more precise hourly electrical power demand modeling in New England. The issue that exists when considering hourly electrical power demand modeling is that these types of plants have a large amount of data. In order to obtain a more precise model of plants with a large amount of data, the main characteristics of the proposed fuzzy logic model are as follows: (1) it is in accordance with the conditions under which a fuzzy logic model and a radial basis mapping model are equivalent to obtain a new scheme, (2) it uses a combination of the descending gradient and the mini-lots approach to avoid applying the descending gradient to all data.

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