Artificial neural networks (ANN) and traditional regression models were developed for prediction of thermal properties of sweet sorghum bagasse as a function of moisture content and room temperature. Predictions were made for three thermal properties: 1) thermal conductivity, 2) volumetric specific heat, and 3) thermal diffusivity. Each thermal property had five levels of moisture content (8.52%, 12.93%, 18.94%, 24.63%, and 28.62%, w. b.) and room temperature as inputs. Data were sub-partitioned for training, testing, and validation of models. Backpropagation (BP) and Kalman Filter (KF) learning algorithms were employed to develop nonparametric models between input and output data sets. Statistical indices including correlation coefficient (R) between actual and predicted outputs were produced for selecting the suitable models. Prediction plots for thermal properties indicated that the ANN models had better accuracy from unseen patterns as compared to regression models. In general, ANN models were able to strongly generalize and interpolate unseen patterns within the domain of training.
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