State Estimation of Catalytic Ozonation by Differential Neural Networks with Discontinuous Learning Law

The aim of this study was to develop an adaptive state estimator armed with discontinuous learning laws for the catalytic ozonation system. A class of differential neural network served to estimate the uncertain section of the uncertain catalytic process. The learning laws used for adjusting the weights included in the neural network based estimator. A set of numerical simulations demonstrated the application of the DNN based state observer and showed the estimation of the non-measurable information in the catalytic ozonation system. The adaptive state estimator with discontinuous learning laws was also evaluated with experimental information. The comparison of suggested and asymptotically convergent DNN based observer demonstrated the superior estimation performance offered by the estimator introduced in this study.