Research on RS-WNN Integrated Modeling

2011 ◽  
Vol 105-107 ◽  
pp. 2169-2173
Author(s):  
Zong Chang Xu ◽  
Xue Qin Tang ◽  
Shu Feng Huang
Keyword(s):  
Neural Network ◽  
Rough Set ◽  
Attribute Reduction ◽  
Wavelet Neural Network ◽  
Integrated Modeling ◽  
Generalization Ability ◽  
Decision Attributes ◽  
Optimum Decision ◽  
Structure Complexity ◽  
Modeling Algorithm

Wavelet Neural Network (WNN) integration modeling based on Rough Set (RS) is studied. An integration modeling algorithm named RS-WNN, which first introduces a heuristic attribute reduction recursion algorithm to determine the optimum decision attributes and then conducts WNN modeling, is proposed. This method is adopted to more effectively eliminate the redundant attributes, lower the structure complexity of WNN, which reduce the time of training and improve the generalization ability of WNN. The result of the experiment shows this method is superior and efficient.

Surface Roughness Prediction of Engineering Ceramics Electro-Spark Machining Based on Rough Set Neural Network

2010 ◽  
Vol 43 ◽  
pp. 269-273
Author(s):  
Xue Li ◽  
He Wang ◽  
Shu Fen Chen
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Processing Parameters ◽  
Relational Model ◽  
Engineering Ceramics ◽  
Roughness Prediction

To solve the problem of difficulty in establishing the mathematical model between process parameters and surface quality in the process of engineering ceramics electro-spark machining, a neural network relational model based on rough set theory is presented. By processing attribute reduction from data sample utilizing rough set theory, defects like bulkiness of neural network structure and difficult convergence etc are aovided when input dimensions is high. A prediction model that a surface roughness varies in accordance with processing parameters in application of well structured neural network rough set is established. Study result shows that utilizing this model can precisely predict surface roughness under the given conditions with little error which proves the reliability of this model.

scholarly journals Construction Of Opinion Models For E-Learning Courses By Rough Set Theory And Text Mining

2019 ◽  
Vol 8 (6) ◽  
pp. 1107-1111 ◽  
Keyword(s):  
Text Mining ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Expressive Power ◽  
Machine Learning Techniques ◽  
Decision Attributes ◽  
E Learning ◽  
Lower And Upper Approximations

Extracting knowledge through the machine learning techniques in general lacks in its predictions the level of perfection with minimal error or accuracy. Recently, researchers have been enjoying the fruits of Rough Set Theory (RST) to uncover the hidden patterns with its simplicity and expressive power. In RST mainly the issue of attribute reduction is tackled through the notion of ‘reducts’ using lower and upper approximations of rough sets based on a given information table with conditional and decision attributes. Hence, while researchers go for dimension reduction they propose many methods among which RST approach shown to be simple and efficient for text mining tasks. The area of text mining has focused on patterns based on text files or corpus, initially preprocessed to identify and remove irrelevant and replicated words without inducing any information loss for the classifying models later generated and tested. In this current work, this hypothesis are taken as core and tested on feedbacks for elearning courses using RST’s attribution reduction and generating distinct models of n-grams and finally the results are presented for selecting final efficient model

Fuzzy Logic and Intelligence System

2000 ◽  
Vol 4 (5) ◽  
pp. 319-320
Author(s):  
Hyung Lee-Kwang ◽  
◽  
Ju-Jang Lee
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Rough Set ◽  
Intelligent Systems ◽  
Fuzzy Logic Controller ◽  
Fuzzy Rule ◽  
Wavelet Neural Network ◽  
Classification Method ◽  
Inference System

These papers are originally published in the proceedings of Korea fuzzy logic and intelligent systems society (KFIS) fall conference in 1999. Eight papers are selected for this special issue. Major topics of them are fuzzy theory, neural network, inference system, intelligent controller, etc. In this issue, Seihwan Park and Hyung Lee-Kwang extend the concept of fuzzy hypergraph to type-2 fuzzy hypergraph using type-2 fuzzy sets. It has not only the same properties of hypergraphs but also the extended properties of them. It is also shown that interval valued fuzzy hypergraph is a special case of type-2 fuzzy hypergraph. Jung-Heum Yon, Yong-Taek Kim, Jae-Yong Seo and Hong-Tae Jeon design an efficient neural network called dynamic multidimensional wavelet neural network. It can perform an effective dynamic mapping with less dimensions of the input signal. These features show one way to compensate the weakness of the diagonal recurrent neural network and feedforward wavelet neural network. Yigon Kim, Yang Hee Jung and Young Chel Bae propose a new method for diagnosis of insulation aging using wavelet. It measures the partial discharge on-line from data acquisition system and analyses it using wavelet to acquire 21) patterns. They design a neuro-fuzzy model that diagnoses an electrical equipment using the data. Byung-Jae Choi, Seong-Woo Kwak and Byung Kook Kim develop an adaptive fuzzy logic controller. A sole input fuzzy variable is used to simplify the design procedure and the switching hyperplane of sliding mode control is used to improve the adaptability. Myung-Geun Chun, Keun-Chang Kwak and Jeong-Woong Ryu show an efficient fuzzy rule generation scheme for adaptive network-based fuzzy inference system using the conditional fuzzy c-means and fuzzy equalization methods. They apply this method to the truck backer-upper control and Box-Jenkins modeling problem. Daijin Kim proposes a new data classification method based on the tolerant rough set that extends the existing equivalent rough set. Twostage classification method is used. All data are classified by using the lower approximation at the first stage and then the non-classified data at the first stage are classified again by using the rough membership functions obtained from the upper approximation set. Min-Soeng Kim, Sun-Gi Hong and Ju-Jang Lee incorporate the Q-learning algorithm into the fuzzy logic controller. Modified fuzzy rule is used for the incorporation. As a result, a fuzzy logic controller is obtained that can learn through experience. Dong Hwa Kim designs a new 2-DOF PID controller and applies it to the operating data based transfer function of Gun-san Gas turbine in Korea. We hope that this issue can be helpful to readers and we appreciate professor Kaoru Hirota for his interest and support for the publication.

Transformer Fault Diagnosis of Rough-Neural Network Based on MEA

2012 ◽  
Vol 217-219 ◽  
pp. 2585-2589
Author(s):  
Jin Lan Gao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rough Set ◽  
Bp Neural Network ◽  
Attribute Reduction ◽  
Global Optimum ◽  
Optimum Solution ◽  
Transformer Fault ◽  
Speed And Accuracy ◽  
Global Optimum Solution

Combining mind evolutionary algorithm with rough set and neural network, this paper proposed a rough set neural network based on MEA for transformer fault diagnosis. Rough set attribute reduction as the front-processor of neural network diagnostic device, and using MEA to search rough set discrete breakpoints and optimize neural network weights and thresholds, it avoided complex manual trial of the conventional rough set attribute reduction and slow convergence speed and low precision of BP neural network, then faster convergence to the global optimum solution and improves the diagnosis speed and accuracy. Simulation results show that this method is effective.

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