An Intelligent Neural Networks System for Prediction of particleboard properties

In the past decade، artificial neural networks have been used as a powerful tool for modeling and prediction in many scientific fields. In this study، the feed-forward multilayer Perceptron (MLP) was utilized and trained by back propagation (BP) algorithm with Levenberg-Marquardt numerical optimization technique via Matlab software. Temperature of press (°C)، mat moisture content (%) and press closing time (sec) were used as inputs، Water absorption (WA2، 24h)، thickness swelling (TS2، 24h) and density were the outputs of neural network model. This technique will increase network versatility and decreases the effect of undesirable and weak data. The modeling and prediction was done based experimental data and the forecasting results were compared with real data. The efficiency of these techniques evaluated with statistical criteria of mean square error (MSE)، root mean square error، (RMSE) and the correlation coefficient (R2). The results showed this ANN model could accurately describe the water absorption، thickness swelling after immersion for 2 and 24 hours، and density of particleboard