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.

scholarly journals Data Mining Algorithm Based on Fuzzy Neural Network

2015 ◽  
Vol 7 (1) ◽  
pp. 1930-1935
Author(s):  
Wu Jianhui ◽  
Su Yu ◽  
Shao Hongbo ◽  
Yin Sufeng ◽  
Xue Ling ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Fuzzy Neural Network ◽  
Data Mining Algorithm ◽  
Mining Algorithm ◽  
Fuzzy Neural

Data mining method based on rough set and fuzzy neural network

2020 ◽  
Vol 38 (4) ◽  
pp. 3717-3725
Author(s):  
Jingyong Zhou ◽  
Yuan Guo ◽  
Yu Sun ◽  
Kai Wu
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Rough Set ◽  
Fuzzy Neural Network ◽  
Mining Method ◽  
Data Mining Method ◽  
Fuzzy Neural

An Intelligent Data Mining System Through Integration Of Electromagnetism-Like Mechanism And Fuzzy Neural Network

2010 ◽  
pp. 61-84
Author(s):  
Peitsang Wu ◽  
Yung-Yao Hung
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Global Optimization ◽  
Fuzzy Neural Network ◽  
Local Optimum ◽  
Sample Point ◽  
Mining System ◽  
The Em Algorithm ◽  
Fuzzy Neural ◽  
Data Mining System

In this chapter, a meta-heuristic algorithm (Electromagnetism-like Mechanism, EM) for global optimization is introduced. The Electromagnetism-like mechanism simulates the electromagnetism theory of physics by considering each sample point to be an electrical charge. The EM algorithm utilizes an attraction-repulsion mechanism to move the sample points towards the optimum. The electromagnetism-like mechanism (EM) can be used as a stand-alone approach or as an accompanying algorithm for other methods. Besides, the electromagnetism-like mechanism is not easily trapped into local optimum. Therefore, the purpose of this chapter is using the electromagnetism-like mechanism (EM) to develop an electromagnetism-like mechanism based fuzzy neural network (EMFNN), and employ the EMFNN to train fuzzy if-then rules.

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.

Sign in / Sign up

Export Citation Format

Share Document