Fine‐Tuning of Pattern net Artificial Neural Network for Classification Based Design of Porous Ceramic Matrix Based Burners: A Comparison of Mean Squared Error and Sum Absolute Error as Performance Function Under Different Hidden Neurons

ABSTRACT A fine‐tuned pattern net artificial neural network (PNANN) is explored for design of porous ceramic matrix (PCM) based burner by using classification model. The PNANN is attempted to fine‐tune by comparing two different performance functions: mean squared error and sum absolute error. Three different numbers of hidden neurons are also tested while using scaled conjugate gradient as training algorithm. The data for the classification model is obtained by simultaneously solving the governing equations of two phases for the porous ceramic matrix (PCM) based burner. Different values of two critical parameters are used in the numerical model. The critical parameters considered for influencing the performance of the PCM based burner are: convective coupling and extinction coefficient. Radiative heat flux is also incorporated in the numerical model by solving radiative transfer equation by discrete transfer method. Based on the difference between the temperature profiles of the two phases (solid and gas), binary array are constructed and used in the classification model. Ten different classes of data signify ten different regime of operation, each having a unique range of values for the critical parameters. All PNANNs with performance function mse are able to give correct identification above 41.3% and upto 81.1%.