An improved OIF Elman neural network based on CSO algorithm and its applications - Zhang, Yufei; Zhao, Jianping; Wang, Limin; Wu, Honggang; Zhou, Ruihong; Yu, Jinglin (Computer communications, 2021)
In order to prevent air pollution and improve the living environment for residents, it is particularly important to carry out air quality forecasting. Air quality is affected by many factors, and showed significant non-linear features. Output–input feedback Elman (OIF Elman) neural network can effectively solve non-linear problems. However, the disadvantages of OIF Elman neural network are easy to fall into local minimum, slow convergence and inflexibility. Chicken swarm optimization (CSO) algorithm has high operating efficiency and fast convergence speed. Therefore, this paper proposes an air pollution prediction model for OIF Elman neural network based on the CSO algorithm (CSO-OIF Elman neural network model). Evaluation indicators are absolute average error and accuracy rate. The efficacy of the proposed model is compared with other models such as traditional Elman neural network model, OIF Elman neural network model and Elman neural network model based on CSO algorithm (CSO-Elman neural network model). The experimental results show that CSO-OIF Elman neural network model has the best accuracy and the smallest absolute average error value, and has higher nonlinear fitting capabilities and generalization capabilities. The establishment of this model can provide useful reference value for atmospheric prediction research.