Computational neural networks for mapping calorimetric data: Application of feed-forward neural networks to kinetic parameters determination and signals filtering

Feedforward neural networks have been used for kinetic parameters determination and signal filtering in differential scanning calorimetry. The proper learning function was chosen and the network topology was optimised using an empiric procedure. The learning process was achieved using various simulated thermoanalytical curves computed for several thermodynamic and kinetic parameters. Various amounts of simulated noise were added on the power signals. The resilient-propagation algorithm led to the best minimisation of the error computed over all the patterns. Relative errors on the thermodynamic and kinetic parameters were evaluated and compared to those obtained with the usual thermal analysis methods. The results obtained are very promising, and the errors are much lower than with usual methods, especially in the presence of noisy signals. This study shows that simulated thermoanalytical curves produced by Joule effect may be used for the deconvolution of the response of the apparatus, by using artificial neural networks.