Tube Inner Circumference State Classification Optimization by Using Artificial Neural Networks, Random Forest and Support Vector Machines Algorithms

Using Artificial Neural Networks, Random Forest and Support Vector Machines algorithms to optimize Tube inner circumference state classification and accomplish the process of Incoming Quality Control (IQC) is proposed in this paper. However, the traditional classification system is usually set the threshold by the developer in the early stages. The method is time-consuming and tedious to develop the module. In modern, machine learning technology can overcome the shortcomings of tradition classification system. However, machine learning exists a lot of algorithms, such as Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), and so on. And, the different algorithms may cause the different characteristics and efficiencies, so it’s necessary to compare the different algorithms at application. This paper will use a method, called grid search to find the best parameter, and compare these algorithms which has the best characteristic, efficiency and the parameter. Finally, it is found from the experimental results that the method of this paper is workable for actual dataset.