A New Pattern Recognition Model based on Heuristic SOM Network and Rough Set Theory

2006 ◽  
Author(s):  
Cuiling Li ◽  
Hao Zhang ◽  
Jian Wang ◽  
Rongyong Zhao
Keyword(s):  
Pattern Recognition ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Recognition Model ◽  
Model Based ◽  
Som Network

scholarly journals Support or Risk? Software Project Risk Assessment Model Based on Rough Set Theory and Backpropagation Neural Network

2019 ◽  
Vol 11 (17) ◽  
pp. 4513 ◽  
Author(s):  
Xiaoqing Li ◽  
Qingquan Jiang ◽  
Maxwell K. Hsu ◽  
Qinglan Chen
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Assessment Model ◽  
Software Project ◽  
Risk Assessment Model ◽  
Backpropagation Neural Network ◽  
Model Based ◽  
Sample Data

Software supports continuous economic growth but has risks of uncertainty. In order to improve the risk-assessing accuracy of software project development, this paper proposes an assessment model based on the combination of backpropagation neural network (BPNN) and rough set theory (RST). First, a risk list with 35 risk factors were grouped into six risk categories via the brainstorming method and the original sample data set was constructed according to the initial risk list. Subsequently, an attribute reduction algorithm of the rough set was used to eliminate the redundancy attributes from the original sample dataset. The input factors of the software project risk assessment model could be reduced from thirty-five to twelve by the attribute reduction. Finally, the refined sample data subset was used to train the BPNN and the test sample data subset was used to verify the trained BPNN. The test results showed that the proposed joint model could achieve a better assessment than the model based only on the BPNN.

The Applications Based on the Rough Set Theory Algorithms to Determine the Weight

2014 ◽  
Vol 989-994 ◽  
pp. 1736-1738
Author(s):  
Ning Ning Chen
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Model Based

This paper puts forward on the Rough Set theory algorithms, and combined it with the method of determining the index weights, then established a weight determined algorithm model based on rough set, Thereby reducing the influence of subjective factors on the weight determination.

Research on Information Technology with Character Pattern Recognition Method Based on Rough Set Theory

2014 ◽  
Vol 886 ◽  
pp. 519-523 ◽  
Author(s):  
Yong Li Liu
Keyword(s):  
Information Technology ◽  
Pattern Recognition ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Decision Rules ◽  
Pattern Recognition Method ◽  
Two Dimensional ◽  
Recognition Method ◽  
Two Dimensional Image

Character Pattern recognition is widely used in the information technology field. This paper proposes a method of character pattern recognition based on rough set theory. By giving the characters two dimensional image, defining the location of the characteristic and abstracting the characteristic value, the knowledge table and table reduction can be ascertained. Then the decision rules can be deduced. Through the simulation of 26 English alphabets, the results illustrate this methods validity and correctness.

scholarly journals On rough sets, their recent extensions and applications

2010 ◽  
Vol 25 (4) ◽  
pp. 365-395 ◽  
Author(s):  
N. Mac Parthaláin ◽  
Q. Shen
Keyword(s):  
Data Mining ◽  
Pattern Recognition ◽  
Set Theory ◽  
Rough Set ◽  
Intelligent Control ◽  
Real World ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Enormous Amount ◽  
Real World Problems

AbstractRough set theory (RST) has enjoyed an enormous amount of attention in recent years and has been applied to many real-world problems including data mining, pattern recognition, and intelligent control. Much research has recently been carried out in respect of both the development of the underlying theory and the application to new problem domains. This paper attempts to summarize the advances in RST, its extensions, and their applications. It also identifies important areas which require further investigation. Typical example application domains are examined which demonstrate the success of the application of RST to a wide variety of areas and disciplines, and which also exhibit the strengths and limitations of the respective underlying approaches.

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