Emotional Neural Network Based on Improved CLPSO Algorithm For Time Series Prediction

In recent years, emotional neural networks (ENNs) have been extensively used in the field of time series prediction. As a variant of ENN, the radial basis emotional neural network (RBENN) is chosen as the prediction model of time series in this paper, because it has a special type of structure that can preprocess the interference in the data. However, it is difficult for many existing methods to determine network structure automatically while adjusting network parameters. To solve this problem, an RBENN based on adaptive inertia weight comprehensive learning particle swarm optimization algorithm (ADw-CLPSO-RBENN) is designed. Firstly, an adaptive inertia weight adjustment strategy based on the CLPSO algorithm (ADw-CLPSO) is exploited to balance the global and local search ability of particles. Secondly, a particle-variable dimensional learning mechanism (PVDLM) is developed based on the ADw-CLPSO algorithm, which enables particles to find the appropriate network structure while searching for the optimal parameter solution. Finally, the proposed method is evaluated in two time series and a real wastewater treatment system. The simulation results demonstrate that the proposed ADw-CLPSO-RBENN can automatically adjust to a suitable network structure, and the prediction accuracy is also better than other methods. Therefore, the proposed method has higher superiority in time series prediction.