Abstract:In order to predict real-time change of Oxidation Reduction Potential (ORP) in production process, a real-time dynamic accurate prediction method was presented for oxidation reduction potential ORP based on theory of phase space reconstruction and optimized Least Squares Support Vector Regression (LSSVR) by learning wolf pack algorithm. Wavelet analysis was used to remove noise from ORP data, and ORP sequence was used to train LSSVR model based on phase space reconstruction. A learning Wolf Swarm Optimization Algorithm was proposed to optimize LSSVR parameters and time window translation method was used to update the model. Feedback correction method was used to revise model predictive output. The experimental results show that the proposed method has a better predictive effect.