A data mining algorithm based on the rough sets theory and BP neural network

2005 ◽  
Author(s):  
Weijin Jiang ◽  
Yusheng Xu ◽  
Yuhui Xu
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Bp Neural Network ◽  
Rough Sets ◽  
Data Mining Algorithm ◽  
Rough Sets Theory ◽  
Mining Algorithm ◽  
Sets Theory

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

DECISION MAKING USING HYBRID ROUGH SETS AND NEURAL NETWORKS

2002 ◽  
Vol 12 (06) ◽  
pp. 435-446 ◽  
Author(s):  
YASSER HASSAN ◽  
EIICHIRO TAZAKI ◽  
SHIN EGAWA ◽  
KAZUHO SUYAMA
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Decision Support ◽  
Rough Sets ◽  
Decision Problem ◽  
Combined System ◽  
Network Systems ◽  
Rough Sets Theory ◽  
Sets Theory

A methodology for using rough sets theory for preference modeling in decision problem is presented in this paper. We will introduce a new method where neural network systems and rough sets theory are completely integrated into a hybrid system and are used cooperatively for decision and classification support. At the first glance, the two methods we discuss have not much in common. But, in spite of the differences between them, it is interesting to try to incorporate both into one combined system, and apply it in the building of a decision support system.

scholarly journals English Feature Recognition Based on GA-BP Neural Network Algorithm and Data Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Dan Wu ◽  
Yuanjun Shen
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Feature Recognition ◽  
Good Effect ◽  
Data Mining Algorithm ◽  
Main Function ◽  
Network Algorithm ◽  
Neural Network Algorithm

With the development of society and the promotion of science and technology, English, as the largest universal language in the world, is used by more and more people. In the life around us, there is information in English all the time. However, because the process of manual recognition of English letters is very labor-intensive and inefficient, the demand for computer recognition of English letters is increasing. This paper studies the influence of the parameters of BP neural network and genetic algorithm on the whole network, including the input, output, and number of hidden layer nodes. Finally, it improves and determines the settings and values of the relevant parameters. On this basis, it shows the rationality of the selected parameters through experiments. The results show that only GA-BP neural network and feature data mining algorithm can complete feature extraction and become the main function of feature classification at the same time. After enough initial data sample analysis training, the GA-BP neural network was found to have good data fault tolerance and feature recognition. The experimental results show that the genetic algorithm can find the best weights and thresholds and the weights and thresholds are given to the BP neural network. After training, the recognition of handwritten letters can be realized. Finally, the convergence of the two algorithms is compared through experiments, which shows that the overall performance of the BP neural network algorithm is improved after genetic algorithm optimization. It can be seen that the genetic algorithm has a good effect in improving the BP neural network and this method has a broad prospect in English feature recognition.

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