The state estimation of the CSTR system based on a recurrent neural network trained by HGAs

The CSTR system (continuous stirred tank reactor system) is a typical nonlinear system. At present, one of its states, reaction consistence, can not be measured. In this paper, a recurrent neural network is used to estimate the value of the state. Nevertheless, due to the strong nonlinearity of the system, traditional training method such as BP algorithm usually converges in local optimum. Genetic algorithms (GAs), as a global optimization search method, can solve the problem, but the conventional GAs converge very slowly. To improve the learning speed of the neural network, a hybrid genetic algorithm (HGA) is employed. The results demonstrate the proposed HGA can get a very good effect