A comparative study of non linear MISO Process modelling techniques: Application to a chemical reactor

This paper proposes the design and a comparative study of two non linear Multiple Input Single Output (MISO) models. The first, titled Volterra model, is built using Volterra series and the second, named RKHS model, uses the Statistical Learning Theory (SLT) which operates on Reproducing Kernel Hilbert Space (RKHS). The complexity of both models is pointed out in SISO models as well as in MISO ones. The performances of both models are evaluated first by using Monte Carlo numerical simulations and second by handling a chemical process known as the Continuous Stirred Tank Reactor CSTR. In both validation operations the results were successful.