Multi-codebook Fuzzy Neural Network Using Incremental Learning for Multimodal Data Classification

2019 ◽  
Author(s):  
M. Anwar Ma'sum ◽  
Wisnu Jatmiko
Keyword(s):  
Neural Network ◽  
Incremental Learning ◽  
Fuzzy Neural Network ◽  
Data Classification ◽  
Multimodal Data ◽  
Fuzzy Neural

scholarly journals Clustering versus Incremental Learning Multi-Codebook Fuzzy Neural Network for Multi-Modal Data Classification

Computation ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 6
Author(s):  
Muhammad Anwar Ma’sum ◽  
Hadaiq Rolis Sanabila ◽  
Petrus Mursanto ◽  
Wisnu Jatmiko
Keyword(s):  
Neural Network ◽  
Incremental Learning ◽  
Fuzzy Neural Network ◽  
Data Classification ◽  
Benchmark Dataset ◽  
Learning Approaches ◽  
Clustering Methods ◽  
Modal Data ◽  
Fuzzy Neural ◽  
Benchmark Datasets

One of the challenges in machine learning is a classification in multi-modal data. The problem needs a customized method as the data has a feature that spreads in several areas. This study proposed a multi-codebook fuzzy neural network classifiers using clustering and incremental learning approaches to deal with multi-modal data classification. The clustering methods used are K-Means and GMM clustering. Experiment result, on a synthetic dataset, the proposed method achieved the highest performance with 84.76% accuracy. Whereas on the benchmark dataset, the proposed method has the highest performance with 79.94% accuracy. The proposed method has 24.9% and 4.7% improvements in synthetic and benchmark datasets respectively compared to the original version. The proposed classifier has better accuracy compared to a popular neural network with 10% and 4.7% margin in synthetic and benchmark dataset respectively.

The Application of Fuzzy Neural Network in Data Mining

2011 ◽  
Vol 179-180 ◽  
pp. 930-935
Author(s):  
Wang Lan Tian
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Fuzzy Neural Network ◽  
Data Classification ◽  
Complex Data ◽  
Spatial Evolution ◽  
Urban Construction ◽  
Fuzzy Neural

Fuzzy neural network, which can deal with complex data and prediction process that other algorithms can not accomplish, has become a focus in recent years in many fields. Data mining can extract such information and knowledge as data classification, spatial evolution and prediction and so on, and in the huge cadastral data find the implied information which is helpful for our urban construction.

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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