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Uncertainty Representation of Ocean Fronts Based on fuzzy-rough set Theory

Journal of Ocean University of China 2008年第2期7卷 131-136页

作者： XUE Cunjin ZHOU Chenghu SU Fenzhen ZHANG Dandan The Marine GIS＇s Center of State Key Laboratory of Resources and Environment Information System Chinese Academy of Sciences Beijing 1 O0101 P. R. China Graduate School of the Chinese Academy of Sciences Beijing 100039 P. R. China

Analysis of ocean fronts＇ uncertainties indicates that they result from indiscemibility of their spatial position and fuzziness of their intensity. In view of this, a flow hierarchy for uncertainty representation of ocean fronts is proposed on the basis of fuzzy-rough set theory. Firstly, raster scanning and blurring are carried out on an ocean front, and the upper and lower approximate sets, the indiscernible relation in fuzzy-rough theories and related operators in fuzzy set theories are adopted to represent its uncertainties, then they are classified into three sets： with members one hundred percent belonging to the ocean front, belonging to the ocean front＇s edge and definitely not belonging to the ocean front. Finally, the approximate precision and roughness degree are utilized to evaluate the ocean front＇s degree of uncertainties and the precision of the representation. It has been proven that the method is not only capable of representing ocean fronts＇ uncertainties, but also provides a new theory and method for uncertainty representation of other oceanic phenomena.

关键词： fuzzy-rough set upper approximate sets lower approximate sets ocean fronts uncertainties

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fuzzy-rough set Based Attribute Reduction with a Simple Fuzzification Method

Fuzzy-Rough Set Based Attribute Reduction with a Simple Fuzz...

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第24届中国控制与决策会议

作者： Xueen Wang 1,2,3 , Deqiang Han 1 , Chongzhao Han 1,2,3 1. Institute of integrated Automation, Xi’an Jiaotong University, Xi’an, 710049, China2. Ministry of Education Key Lab for Intelligent Networks and Network Security, Xi’an Jiaotong University, Xi’an, 710049, China3. State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

The fuzzy-rough set based attribute reduction, which can get better reducts than the crisp rough set approach, has been paid more attention recently. Fuzzification is a step of data preprocess which was studied less in the application of fuzzy-rough set. In this paper, a simple fuzzification method deriving fuzzy discretization from K most important cuts in the application of feature selection is proposed. A comparative experiment between the proposed fuzzification method and a general fuzzy c-means based method is constructed on the UCI machine learning data repository. The experimental results show the obtained reducts using the proposed method can get higher classification accuracies and less number of selected attributes.

关键词： Feature Selection Information Entropy fuzzy-rough set Fuzzification

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Hand Gesture Recognition Based on Improved FRNN

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Journal of Donghua University(English Edition) 2005年第5期22卷 47-52页

作者：滕晓龙王向阳刘重庆 Institute of Image processing and Pattern recognition Shanghai Jiaotong University

The trained Gaussian mixture model is used to make skincolour segmentation for the input image sequences. The hand gesture region is extracted, and the relative normalization images are obtained by interpolation operation. To solve the proem of hand gesture recognition, fuzzy-rough based nearest neighbour（RNN） algorithm is applied for classification. For avoiding the costly compute, an improved nearest neighbour classification algorithm based on fuzzy-rough set theory （FRNNC） is proposed. The algorithm employs the represented cluster points instead of the whole training samples, and takes the hand gesture data＇s fuzziness and the roughness into account, so the campute spending is decreased and the recognition rate is increased. The 30 gestures in Chinese sign language alphabet are used for approving the effectiveness of the proposed algorithm. The recognition rate is 94.96%, which is better than that of KNN （K nearest neighbor）and fuzzy- KNN （fuzzy K nearest neighbor）.

关键词： fuzzy-rough set edit nearest neighbour algorithm hand gesture recognition

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Multidimensional LSTM Networks to Predict Wind Speed

Multidimensional LSTM Networks to Predict Wind Speed

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第37届中国控制会议

作者： Wanye Yao Pu Huang Zhaoxin Jia North China Electric Power University

Wind speed forecasting has great significance to the improvement of wind turbine intelligent control technology and the stable operation of power *** this paper,the Long Short-term Memory（LSTM） mode combined with deep learning ability which with the fuzzy-rough set theory has been proposed for short-term wind speed *** the fuzzy-rough set to reduce input and space features *** find the main factors that influencing the wind speed as the input for the LSTM neural network prediction *** achieve the forecast of data trends through the strong generalization ability on massive of data *** experimental results show that the FRS-LSTM model has higher prediction accuracy than traditional neural network.

关键词： LSTM deep learning fuzzy-rough set wind speed prediction

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