Using support vector machine for characteristics prediction of hydraulic valve

Accurate prediction for the synthesis characteristics of a hydraulic valve plays an important role in decreasing the repair and reject rate of the hydraulic product. Recently, intelligence system approaches such as Artificial Neural Network (ANN) and neuro-fuzzy methods have been used successfully for system modelling. The major shortcomings of these approaches are that a large number of training data sets are needed or the training time is too long. Using Support Vector Machine (SVM) approaches would help to overcome these issues. In this study, the SVM approach was used to construct a hydraulic valve characteristics forecasting system. To illustrate the applicability and capability of the SVM, a specific hydraulic valve production was selected as a case study. The prediction results showed that the proposed prediction method was more applicable and has higher accuracy than adaptive neuro-fuzzy inference system (ANFIS) and ANN in predicting the synthesis characteristics of hydraulic valve.