The nature of statistical learning theory
The nature of statistical learning theory
Neural Computation
Expert Systems with Applications: An International Journal
Facing classification problems with Particle Swarm Optimization
Applied Soft Computing
Type-2 fuzzy logic-based classifier fusion for support vector machines
Applied Soft Computing
Expert Systems with Applications: An International Journal
Classification model for product form design using fuzzy support vector machines
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
A distributed PSO-SVM hybrid system with feature selection and parameter optimization
Applied Soft Computing
Expert Systems with Applications: An International Journal
Fault diagnosis of pneumatic systems with artificial neural network algorithms
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
Information Sciences: an International Journal
Hi-index | 12.05 |
This paper presents a new version of fuzzy support vector classifier machine to diagnose the nonlinear fuzzy fault system with multi-dimensional input variables. Since there exist problems of finite samples and uncertain data in complex fuzzy fault system modeling, the input and output variables are described as fuzzy numbers. Then by integrating the fuzzy theory and v-support vector classifier machine, the triangular fuzzy v-support vector regression machine (TF v-SVCM) is proposed. To seek the optimal parameters of TF v-SVCM, particle swarm optimization (PSO) is also applied to optimize parameters of TF v-SVCM. A diagnosing method based on TF v-SVCM and PSO are put forward. The results of the application in fault system diagnosis confirm the feasibility and the validity of the diagnosing method. The results of application in fault diagnosis of car assembly line show the hybrid diagnosis model based on TF v-SVCM and PSO is feasible and effective, and the comparison between the method proposed in this paper and other ones is also given, which proves this method is better than standard v-SVCM.