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The well-known support vector data description (SVDD) is based on precise description of precise data. When we know the features of training samples precisely and we are uncertain about their class labels, the fuzzy SVDD can be used to obtain the data description. But if the features of training samples are fuzzy numbers, the fuzzy SVDD cannot be utilized.(More)
A fuzzy min–max neural network with symmetric margin (FMNWSM) is proposed in this paper. Therefore, its probability of misclassification is lower than traditional fuzzy min–max neural networks if both training and test samples are from identical probability distribution. Meanwhile, data is classified with symmetric margin by the use of a non-linear program(More)
Because the SVM (support vector machine) classifies data with the widest symmetric margin to decrease the probability of the test error, modern fuzzy systems use SVM to tune the parameters of fuzzy if-then rules. But, solving the SVM model is time-consuming. To overcome this disadvantage, we propose a rapid method to solve the robust SVM model and use it to(More)
  • Y. Forghani
  • 2008
Nodes in a sensor network are often randomly distributed. To assign measurements to locations, each node has to determine its own position. Algorithms for positioning in sensor networks are classified into two groups: approximate and exact. In this paper, we propose a range-based approximate positioning approach. Then, compare it with another approximate(More)
  • Y. Forghani
  • 2008
Nodes in a sensor network are often randomly distributed. To assign measurements to locations, each node has to determine its own position. Algorithms for positioning in wireless sensor networks are classified into two groups: approximate and exact. In this paper, we propose a range-based approximate positioning approach which is almost the combination of(More)
This paper comments on the published work dealing with robustness and regularization of 2009) by H. Xu, etc. They proposed a theorem to show that it is possible to relate robustness in the feature space and robustness in the sample space directly. In this paper, we propose a counter example that rejects their theorem. 1. Comment Firstly, it must be stated(More)