Aiming at the problem of recurrent radial basis function (RRBF) neural network structure which is difficult to be self-adaptive

this paper proposes a structure design method based on recursive orthogonal least square (ROLS) algorithm. Firstly

ROLS algorithm is used to calculate the contribution and the loss function of hidden layer neurons

which determines to increase or be grouped into inactive neurons

and the topology structure of neural network is adjusted accordingly. At the same time

singular value decomposition (SVD) is applied to determine the best number of hidden layer neurons in order to delete the neurons of the inactive group

which effectively solves the problems of RRBF neural network structure which is redundant and hardly self-adaptive. Secondly

the gradient descent algorithm is utilized to update the parameters of RRBF neural network in order to ensure the accuracy of neural network. Finally

several experiments including the Mackey-Glass time series prediction

nonlinear system identification and key water quality parameters dynamic modeling in wastewater treatment process are conducted

and the simulation results prove the feasibility and effectiveness of the structure design method.