An evolved recurrent neural network and its application in the state estimation of the CSTR system

Continuous Stirred Tank Reactor System (CSTR) is a typical chemical reactor system with a complex nonlinear dynamic characteristics. In this paper, a recurrent neural network (RNN) evolved by a cooperative scheme is proposed to estimate the state of the CSTR system, which combines the architectural evolution with weight learning. In this scheme, particle swarm optimization (PSO) adaptively constructs the network architectures, then evolutionary algorithm (EA) is employed to evolve the network nodes with this architecture, and this process is automatically alternated. It can effectively alleviate the noisy fitness evaluation problem and the moving target problem. In addition of these, a closer behavioral link between the parents and their offspring is maintained, which improves the efficiency Of evolving RAW. The results show that the proposed scheme is able to evolve both the architecture and weights of RAW, and the effectiveness and efficiency is better than the algorithms of TDRB, GA, PSO, and HGAPSO applied to the fully connected RAW.