HHVannPRO: ANN trained to predict the biomass HHV based on the results of the proximate analysis

For the comparative analysis of different algorithms for training the ANNs the same network structure is used but with diferent training functions. In all cases this network was fed with the same sets of input data. Data sets were formed on the basis of literature data on measured HHV of biomass characterized by the proximate analysis. In order to improve the ANNs response, the training data sets were augmented with the additional literature data on measured HHV and biomass composition in terms of the proximate analysis, so that the final set of data consisted of 318 training input records and corresponding 318 outputs. The custom designed MATLAB functions were then applied to a new set of input data completely unknown to ANNs and the results were recorded. Folder ANNmatlabDefault comprises files related to training functions Levenberg-Marquardt, Bayesian Regularization ans Scaled Conjugate Gradient. Other folders are related to other training functions: Gradient Descent, Gradient Descent with Momentum, Variable Learning Rate Gradient Descent, One Step Secant, Polak-Ribiére Conjugate Gradient, Fletcher-Powell Conjugate Gradient, Conjugate Gradient with Powell/Beale Restarts, Resilient Backpropagation and BFGS Quasi-Newton.