A self-organizing fuzzy neural network (SOFNN) control method is proposed

and its application system is designed for controlling the dissolved oxygen (DO) concentration in the activated sludge wastewater treatment processes. The neurons of rule layer are grown or pruned adaptively based on firing strength and mutual information to meet dynamic change of the real operating condition. Meanwhile

the centers and widths of membership functions and weights of output layer are trained by gradient descent optimization algorithm to ensure the convergence of SOFNN. The stability of the control system is proved based on the analysis of the learning rates of parameters in SOFNN by applying the Lyapunov stability theory. This control strategy is investigated and evaluated based on international Benchmark Simulation Model No.1 (BSM1). Experimental results demonstrate that the SOFNN controller performs better than PID

fuzzy logical control (FLC)

model predictive control (MPC)

and some other existing control methods. Performance comparisons indicate that the proposed SOFNN control strategy obtains higher tracking accuracy

better control placidity and superior adaptive capability.