Hybrid neural network approach in description and prediction of dynamic behavior of chaotic chemical reaction systems

Abstract

A chaotic system with available prior knowledge is identified with both the sequential hybrid neural network and the standard Artificial Neural Network (ANN). The identified models are validated with phase portrait, return map, the largest Lyapunov exponent and correlation dimension instead of using Sum of Square Errors (SSE). Interpolation and Extrapolation capability of the models are compared. This is demonstrated for nonisothermal, irreversible, first-order, series reaction A≇B≇C in a CSTR.