A new method of Data Mining based on fuzzy neural network

Author(s):  
Xi Li ◽  
Yujie Zheng
2015 ◽  
Vol 7 (1) ◽  
pp. 1930-1935
Author(s):  
Wu Jianhui ◽  
Su Yu ◽  
Shao Hongbo ◽  
Yin Sufeng ◽  
Xue Ling ◽  
...  

2020 ◽  
Vol 38 (4) ◽  
pp. 3717-3725
Author(s):  
Jingyong Zhou ◽  
Yuan Guo ◽  
Yu Sun ◽  
Kai Wu

2011 ◽  
Vol 179-180 ◽  
pp. 930-935
Author(s):  
Wang Lan Tian

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.


Author(s):  
Peitsang Wu ◽  
Yung-Yao Hung

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.


2018 ◽  
Vol 120 ◽  
pp. 220-230 ◽  
Author(s):  
Amir Sharifian ◽  
M. Jabbari Ghadi ◽  
Sahand Ghavidel ◽  
Li Li ◽  
Jiangfeng Zhang

2012 ◽  
Vol 433-440 ◽  
pp. 2509-2512 ◽  
Author(s):  
Li Na Liu ◽  
Hui Juan Qi ◽  
De Xiong Li

This paper introduces the concept of data mining generally and summarizes several methods of data mining, and presents a data mining algorithm based on fuzzy neural network (FNN). Using fuzzy theory and neural network to structure and train fuzzy neural network, the algorithm overcomes the shortcomings of neural network such as complex structure, long training time and lack of understandable representation of results.


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