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A weighted ls-svm approach for the identification of a class of nonlinear inverse systems

Science in China(Series F) 2009年第5期52卷 770-779页

作者： SUN ChangYin MU ChaoXu LI XunMing School of Automation Southeast University Nanjing 210096 China College of Electrical Engineering Hohai University Nanjing 210098 China

In this paper, a weighted least square support vector machine algorithm for identification is proposed based on the T-S model. The method adopts fuzzy c-means clustering to identify the structure. Based on clustering, the original input/output space is divided into several subspaces and submodels are identified by least square support vector machine （ls-svm）. Then, a regression model is constructed by combining these submodels with a weighted mechanism. Furthermore we adopt the method to identify a class of inverse systems with immeasurable state variables. In the process of identification, an allied inverse system is constructed to obtain enough information for modeling. Simulation experiments show that the proposed method can identify the nonlinear allied inverse system effectively and provides satisfactory accuracy and good generalization.

关键词： subspaces weighted ls-svm inverse system nonlinear modeling

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A double weighted ls-svm model for data estimation in wireless sensor networks

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Journal of Beijing Institute of Technology 2012年第1期21卷 134-139页

作者：谢迎新陈祥光赵军 School of Chemical Engineering and Environment Beijing Institute of Technology

In wireless sensor networks, data missing is a common problem due to sensor faults, time synchronization, malicious attacks, and communication malfunctions, which may degrade the network＇ s performance or lead to inefficient decisions. Therefore, it is necessary to effectively estimate the missing data. A double weighted least squares support vector machines （DWls-svm） model for the missing data estimation in wireless sensor networks is proposed in this paper. The algo- rithm first applies the weighted ls-svm （Wls-svm） to estimate the missing data on temporal do- main and spatial domain respectively, and then uses the weighted average of these two candidates as the final estimated value. DWls-svm considers the possibility of outliers in the dataset and utilizes spatio-temporal dependencies among sensor nodes fully, which makes the estimate more robust and precise. Experimental results on real world dataset demonstrate that the proposed algorithm is outli- er robust and can estimate the missing values accurately.

关键词： wireless sensor networks weighted ls-svm spatio-temporal dependencies missing data

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