Comparison of predicted thickness swelling of particleboard with fuzzy systems and artificial neural networks

Swelling percent is a very important physical property of the final product. Swelling test is timely and needed to cost. Therefore, swelling prediction of particleboard in production during can operate of the process line and consistent quality of production. In this research, variables such as moisture content of particle, the amount of adhesive, press time, press temperature, press pressure and swelling properties of particleboard were collected from Debalkhazae mill. The normalized data was analyzed by artificial neural network and fuzzy systems. Also, swelling percent was predicted by optimal model. The best model of swelling prediction is 5-5 basis of artificial neural networks, and the best function of fuzzy systems is Z-shaped curve membership function. Means absolute percent errors of the predictions are equal 5 and 22 percent, respectively. ANN method has better performance compared with fuzzy systems.