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A new method of fault diagnosis by dynamic modeling is put forward to obtain the diagnosis parameters of intelligent appliance in different running stages. The model identification approach using support vector regression (SVR) and immune clone selection algorithm (ICSA) is presented in this paper. The relation between process status and the temperature change rate is analyzed in the paper. For appliance...
A new fuzzy identification approach using support vector regression (SVR) and particle swarm optimization algorithm (PSOA) is presented in this paper. Firstly positive definite reference function is utilized to construct a qualified Mercer kernel for SVR. Then an improved PSOA is developed for parameters selection of SVR, in which the number of support vectors and regression accuracy are regarded...
In the analysis of electronic circuit fault diagnosis based on support vector regression (SVR), irrelevant or correlated features in the samples could spoil the performance of the SVR classifier, leading to decrease of prediction accuracy. In order to solve the problems mentioned above, this paper used rough sets as a preprocessor of SVR to select a subset of input variables and employed the particle...
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