A self-augmented radial basis function neural network for sensitive systems

Abstract

In order to solve the imprecise problem of radial basis function neural network (RBFNN) to the sample output for the sensitive system, a self-augmented RBFNN (SA-RBFNN) is designed to improve the accuracy of the model. Firstly, the network structure is constructed by using the correlation between input, output and hidden layer neurons to increase the sensitivity of the network. Secondly, an accelerated gradient algorithm is used to train SA-RBFNN, which improves the speed and the precision of neural network training. Finally, the proposed SA-RBFNN is evaluated through a benchmark experiment and a practical problem in wastewater treatment process. The results indicate that the proposed SA-RBFNN can quickly converge to the optimal solution and has a good effect on more sensitive systems.