Modelling of Membrane Fouling by PCA-PSOBP Neural Network

In this paper, the PCA-PSOBP neural network has been put forward to model ultrafiltration of printing and dyeing wastewater. Firstly, Principal Component Analysis (PCA) was applied to reduce the dimensions and correlations of input parameters. Secondly, the PSOBP was used to optimize the weights and thresholds of the neural networks, in which weights of BP neural network were adjusted by particle swarm optimization (PSO) rather than traditional gradient descent method. Based on experimental data, simulations are performed with MATLAB. The results showed that PCA-PSOBP neural network has a faster convergence speed and a better agreement with the real data than traditional BP neural network.