Intelligent Forecasting on Composite Regeneration Endpoint of DPF in the Vehicle
A mathematical model based on fuzzy least squares support vector machines has been developed to forecast the composite regeneration endpoint of the diesel particulate filter in Vehicle. The results show that the relative error of forecasting model of composite regeneration endpoint is less than 3.5%, and the oxygen concentration of exhaust gas has the biggest effect on the endpoint of composite regeneration, followed by the mass flow rate of exhaust gas, the microwave power, the temperature of exhaust gas and the amount of cerium-based additive.
Classification Identification of Acoustic Emission Signals from Underground Metal Mine Rock by ICIMF Classifier
To overcome the drawback that fuzzy classifier was sensitive to noises and outliers, Mamdani fuzzy classifier based on improved chaos immune algorithm was developed, in which bilateral Gaussian membership function parameters were set as constraint conditions and the indexes of fuzzy classification effectiveness and number of correct samples of fuzzy classification as the subgoal of fitness function. Moreover, Iris database was used for simulation experiment, classification, and recognition of acoustic emission signals and interference signals from stope wall rock of underground metal mines. The results showed that Mamdani fuzzy classifier based on improved chaos immune algorithm could effectively improve the prediction accuracy of classification of data sets with noises and outliers and the classification accuracy of acoustic emission signal and interference signal from stope wall rock of underground metal mines was 90.00%. It was obvious that the improved chaos immune Mamdani fuzzy (ICIMF) classifier was useful for accurate diagnosis of acoustic emission signal and interference signal from stope wall rock of underground metal mines.
Research and Application of Support Vector Machine
A support vector machine is a new machine learning technique developed on the basis of statistical learning theory, and it is the most successful realization of statistical learning theory. For machine learning tasks involving pattern classification, regression estimation, and function approximation, the support vector machine has increasingly become a popular tool. In this paper a survey of the recent new development on the research and application of the support vector machines is made. Some important issues to be investigated in application and the direction of research of the support vector machine have been pointed out simultaneously.