Modeling of nonlinear chemical kinetics was performed using neural networks maps. These networks based on a simple multivariate polynomial architecture are useful in approximating a wide variety of chemical kinetic systems. The accuracy and efficiency of these ridge polynomial networks (RPN) were demonstrated by modeling the kinetics of H 2 bromination, formaldehyde oxidation and H 2 + O 2 combustion. RPN networks are found to provide excellent approximations to complex kinetic models.
|Original language||English (US)|
|Number of pages||10|
|Journal||Journal of Chemical Physics|
|State||Published - Jun 1 2004|
All Science Journal Classification (ASJC) codes
- Physics and Astronomy(all)
- Physical and Theoretical Chemistry