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.