A Self-organizing Recurrent Neural Network Based on Dynamic Analysis

A recurrent neural network with a self-organizing structure based on the dynamic analysis of a task is presented in this paper. The stability of the recurrent neural network is guaranteed by design. A dynamic analysis method to sequence the subsystems of the recurrent neural network according to the fitness between the subsystems and the target system is developed. The network is trained with the network's structure self-organized by dynamically activating subsystems of the network according to tasks. The experiments showed the proposed network is capable of activating appropriate subsystems to approximate different nonlinear dynamic systems regardless of the inputs. When the network was applied to the problem of simultaneously soft measuring the chemical oxygen demand (COD) and NH3-N in wastewater treatment process, it showed its ability of avoiding the coupling influence of the two parameters and thus achieved a more desirable outcome.