# Prediction Research on Irregularly Cavited Components Volume Based on Gray Correlation and PSO-SVM
###### Abstract  
The use of a micro-compressed air-volume-detection method to detect the volume of irregularly cavited components has the characteristics of multi-variable coupling and nonlinearity. To solve this problem, a volume-prediction model of irregularly cavited components based on gray correlation and a particle-swarm-optimization support-vector machine is proposed. In this paper, the gray-correlation method was used to extract the detection parameters that have the greatest correlation with the cavity volume. On the basis of the obtained detection parameters, the SVM algorithm was used to build an irregularly cavited components volume-prediction model. During model training, since the regression accuracy and generalization performance of the SVM model depend on the proper setting of its two parameters (the penalty-parameter C and the kernel-parameter \(\sigma\)), and especially on the interaction of the parameters, this paper presents an optimal-selection approach towards the SVM parameters, based on the particle-swarm-optimization (PSO) algorithm. Experiments showed that the prediction model can better predict the volume of irregularly cavited components, and the prediction accuracy  
high requirements for test conditions, and do not consider the influence of air humidity and temperature on the measurement results; however, it is difficult to meet these conditions in the actual application process.  
In view of the practical problems of the above detection-methods, this paper analyzed the structural characteristics of the irregularly cavitied components, applied the ideal-gas equation-of-state, adopted the volume-measurement method of micro-compressed air, and, fully considering the influence of environmental factors such as temperature, humidity, pressure and gas-equilibrium time on volume measurement, a volume-prediction algorithm for irregularly cavitied components based on gray correlation and particle-swarm-optimization support vector machine was proposed, which was used for volume detection of irregularly cavitied components. The method has the characteristics of high efficiency and high precision, and has reference significance for the volume-prediction and detection of irregularly cavitied components.  
The main contributions of this paper are three aspects. Firstly, collecting the parameters related to the volume of the irregularly cavitied components by using the micro-compressed-air method, under the premise of ensuring that the components to be tested are not damaged, this method micro-compresses the atmospheric pressure of the air sealed in the irregularly cavited components, and collects the measurement parameters. Secondly, the main control-factors affecting the irregularly cavited components' volume-measurement are analyzed by gray correlation, and we screen pressure, temperature, humidity, and air-equilibrium time, etc., as the main characteristic parameters. Thirdly, there is the development of a PSO-SVM model, to improve the accuracy and stability of the volume-prediction model of irregularly cavitied components. Finally, obtaining the volume-prediction value of the irregularly cavited components, means that the volume of irregularly cavited components can be measured quickly and accurately.