Sentence examples for fuzzy means from inspiring English sources
The main contribution of this work is the proposal for a fuzzy keyword search over encrypted data where fuzzy means that we tolerate some edit distance deviation.
The first swarm applies the non-symmetric fuzzy means algorithm to calculate the network structure and RBF kernel center coordinates, while the second encodes the basis function widths by introducing a modified neighbor coverage heuristic.
More specifically, the Radial Basis Function (RBF) neural network architecture serves as the nonlinear modeling tool, by exploiting the simplicity of its topology and the fast fuzzy means training algorithm.
More specifically, the fuzzy means algorithm is employed on a set of training data, where the input data refer to variables that are measured on-line and the output data correspond to quality variables that are classified by human experts.
For strong faults and weak faults of bearing in this study, 320 samples gained from an experimental roller bearing are respectively adopted to verify the superior diagnostic performance of the SSI-MSVM method compared with that of K-means clustering, fuzzy means clustering (FCM) and traditional SVM.
The results indicate that the SSI-MSVM method is effective in fault diagnosis for a wind turbine bearing and can successfully identify fault types of bearing and achieve higher diagnostic accuracy than that of K-means clustering, fuzzy means clustering and traditional SVM.
Based on the concept of semivariance of fuzzy variable, two fuzzy mean-semivariance models are proposed.
Cheng (1998) improved the concept of ranking fuzzy numbers using fuzzy mean and fuzzy standard deviation as proposed by Lee and Li (1988).
An adaptive weighted fuzzy mean (AWFM) filter with high stability is proposed for signal restoration in this paper.
As an extension of the fuzzy mean-variance model, a mean-variance-skewness model is presented and the corresponding variations are also considered.
The aggregate weights calculated by employing Eqs. 1, 2, 3 and 4, which are listed in the fuzzy, mean column of Table 11.
