The nature of statistical learning theory
The nature of statistical learning theory
Neural Computation
Type-2 fuzzy logic-based classifier fusion for support vector machines
Applied Soft Computing
Classification model for product form design using fuzzy support vector machines
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
The forecasting model based on wavelet ν-support vector machine
Expert Systems with Applications: An International Journal
Fault diagnosis of power transformer based on support vector machine with genetic algorithm
Expert Systems with Applications: An International Journal
Regression application based on fuzzy ν-support vector machine in symmetric triangular fuzzy space
Expert Systems with Applications: An International Journal
Journal of Computational and Applied Mathematics
Hybrid model based on SVM with Gaussian loss function and adaptive Gaussian PSO
Engineering Applications of Artificial Intelligence
The Kernel Common Vector Method: A Novel Nonlinear Subspace Classifier for Pattern Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Two Criteria for Model Selection in Multiclass Support Vector Machines
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Estimation of number of people in crowded scenes using perspective transformation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
An overview of statistical learning theory
IEEE Transactions on Neural Networks
Content-based audio classification and retrieval by support vector machines
IEEE Transactions on Neural Networks
Hi-index | 12.05 |
This paper presents a new version of fuzzy wavelet support vector classifier machine to diagnosing the nonlinear fuzzy fault system with multi-dimensional input variables. Since there exist some problems of Gaussian noise and uncertain data in complex fuzzy fault system, the input and output variables are described as fuzzy numbers. Then by integrating the fuzzy theory, wavelet analysis theory, Gaussian loss function and @n-support vector classifier machine, fuzzy Gaussian wavelet @n-support vector classifier machine (TFGW v-SVCM) is proposed. To seek the optimal parameters of TFGW v-SVCM, genetic algorithm (GA) is presented to optimize the unknown parameters of TFGW v-SVCM. A diagnosing method based on TFGW v-SVCM and GA is presented. The results of the application in car assembly line diagnosis confirm the feasibility and the validity of the diagnosing method. Compared with the traditional model and other SVCM methods, TFGW v-SVCM method requires fewer samples and has better diagnosing precision.