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

The CSTR system (The 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 non-linearity 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 very good effect.