The nature of statistical learning theory
The nature of statistical learning theory
Fuzzy least squares support vector machines for multiclass problems
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
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
A Fuzzy Support Vector Machine with Weighted Margin for Flight Delay Early Warning
FSKD '08 Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03
Image Classification Based on Fuzzy Support Vector Machine
ISCID '08 Proceedings of the 2008 International Symposium on Computational Intelligence and Design - Volume 01
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
Power load forecasts based on hybrid PSO with Gaussian and adaptive mutation and Wv-SVM
Expert Systems with Applications: An International Journal
The hybrid forecasting model based on chaotic mapping, genetic algorithm and support vector machine
Expert Systems with Applications: An International Journal
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
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In view of the shortage of @e-insensitive loss function for Gaussian noise, this paper presents a new version of fuzzy support vector machine (SVM) which can penalize Gaussian noise to forecast fuzzy nonlinear system. Since there exist some problems of finite samples and uncertain data in many forecasting problem, the input variables are described as crisp numbers by fuzzy comprehensive evaluation. To represent the fuzzy degree of these input variables, the symmetric triangular fuzzy technique is adopted. Then by the integration of the fuzzy theory, @n-SVM and Gaussian loss function theory, the fuzzy @n-SVM with Gaussian loss function (Fg-SVM) which can penalize Gaussian noise is proposed. To seek the optimal parameters of Fg-SVM, genetic algorithm is also proposed to optimize the unknown parameters of Fg-SVM. The results of the application in sale system forecasts confirm the feasibility and the validity of the Fg-SVM model. Compared with the traditional model, Fg-SVM method requires fewer samples and has better generalization capability for Gaussian noise.