A locally recurrent fuzzy neural network with support vector regression for dynamic-system modeling
IEEE Transactions on Fuzzy Systems
Electric load forecasting based on locally weighted support vector regression
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An interval type-2 fuzzy-neural network with support-vector regression for noisy regression problems
IEEE Transactions on Fuzzy Systems
Revenue forecasting using a least-squares support vector regression model in a fuzzy environment
Information Sciences: an International Journal
Information Sciences: an International Journal
Improving project-profit prediction using a two-stage forecasting system
Computers and Industrial Engineering
Tourism demand forecasting using novel hybrid system
Expert Systems with Applications: An International Journal
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To achieve the desired quality in plastic injection molding, advanced monitoring techniques are often recommended in the workshop. Unfortunately, the signal in plastic injection modeling process such as nozzle pressure that is relevant to part quality is not easy to obtain because of the cost of sensors. The sensor-based modeling idea is therefore adopted. In this paper, a new method for predicting the parts weight in plastic injection molding using least squares support vector regression (LS-SVR) is proposed, which is composed of two steps. The first step is to estimate the nozzle pressure with the hydraulic system pressure using an LS-SVR model. The second step is to predict product weight using the estimated nozzle pressure, which is done using another LS-SVR model. The experimental results show that the new method is very effective.