The nature of statistical learning theory
The nature of statistical learning theory
Predicting Time Series with Support Vector Machines
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Data preparation for sample-based face detection
International Journal of Computer Applications in Technology
International Journal of Computer Applications in Technology
GenSoFNN: a generic self-organizing fuzzy neural network
IEEE Transactions on Neural Networks
International Journal of Computer Applications in Technology
Ensemble learning for generalised eigenvalues proximal support vector machines
International Journal of Computer Applications in Technology
Domain information based prediction of protein-protein interactions of glucosinolate biosynthesis
International Journal of Computer Applications in Technology
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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.