A Mathematical model to predict particleboard properties using the GMDH-type neural network and genetic algorithm

In this study the GMDH neural network based on genetic algorithm to predict the physical and mechanical properties of particleboard laboratory scale has been used. predict the mechanical and physical properties of particleboard we used input parameters such as neural network including time of press closing (10،20 and 30) s، moisture mat (8،10،12 and 14) % and temperature press (150،160،170 and 180) °C as the input data and the output data is used as the physical and mechanical properties. The efficiency of these techniques evaluated with statistical criteria of mean square error (MSE)، root mean square error، (RMSE)، mean absolute deviation (MAD) and the correlation coefficient (R2). Results showed that the values of MSE، RMSE and MAD for properties MOR، IB، TS24h، TS2h، WA2h and WA24h is low. Errors were obtained for the MOE model was very high. According to the values obtained from MOE is not the appropriate model to predict. R2 values obtained from the test and training set properties for MOR، IB، MOE، TS24h، TS2h، WA2h and WA24h more than 0. 91%، Which reflects the performance of these models is better.