Data-driven design of the extended fuzzy neural network having linguistic outputs

2018 ◽  
Vol 34 (1) ◽  
pp. 349-360 ◽  
Author(s):  
Chengdong Li ◽  
Zixiang Ding ◽  
Dianwei Qian ◽  
Yisheng Lv
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Data Driven ◽  
Fuzzy Neural

Data driven modeling based on dynamic parsimonious fuzzy neural network

2013 ◽  
Vol 110 ◽  
pp. 18-28 ◽  
Author(s):  
Mahardhika Pratama ◽  
Meng Joo Er ◽  
Xiang Li ◽  
Richard J. Oentaryo ◽  
Edwin Lughofer ◽  
...  
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Data Driven ◽  
Fuzzy Neural ◽  
Data Driven Modeling

Data driven modelling based on Recurrent Interval-Valued Metacognitive Scaffolding Fuzzy Neural Network

2017 ◽  
Vol 262 ◽  
pp. 4-27 ◽  
Author(s):  
Mahardhika Pratama ◽  
Edwin Lughofer ◽  
Meng Joo Er ◽  
Sreenatha Anavatti ◽  
Chee-Peng Lim
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Data Driven ◽  
Fuzzy Neural ◽  
Metacognitive Scaffolding ◽  
Recurrent Interval ◽  
Interval Valued

scholarly journals A fuzzy neural network model to forecast the percent cloud coverage and cloud top temperature maps

2008 ◽  
Vol 26 (12) ◽  
pp. 3945-3954 ◽  
Author(s):  
Y. Tulunay ◽  
E. T. Şenalp ◽  
Ş. Öz ◽  
L. I. Dorman ◽  
E. Tulunay ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Fuzzy Neural Network ◽  
Data Driven ◽  
Mapping Technique ◽  
Inference System ◽  
Cloud Coverage ◽  
Highly Nonlinear ◽  
Fuzzy Neural

Abstract. Atmospheric processes are highly nonlinear. A small group at the METU in Ankara has been working on a fuzzy data driven generic model of nonlinear processes. The model developed is called the Middle East Technical University Fuzzy Neural Network Model (METU-FNN-M). The METU-FNN-M consists of a Fuzzy Inference System (METU-FIS), a data driven Neural Network module (METU-FNN) of one hidden layer and several neurons, and a mapping module, which employs the Bezier Surface Mapping technique. In this paper, the percent cloud coverage (%CC) and cloud top temperatures (CTT) are forecast one month ahead of time at 96 grid locations. The probable influence of cosmic rays and sunspot numbers on cloudiness is considered by using the METU-FNN-M.

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

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