The Kalman Particle Swarm Optimization Algorithm and Its Application in Soft-sensor of Acrylonitrile Yield

This paper proposes the Kalman particle swarm optimization algorithm (KPSO), which combines the Kalman filter and PSO. KPSO assumes that particle moves according to the Kalman filter. The comparison of optimization performance between KPSO and PSO to three widely used test functions shows that the optimization performance of KPSO is much better than that of PSO. The combination of KPSO and ANN is also introduced (KPSONN). Then, KPSONN is applied to construct a practical soft-sensor of acrylonitrile yield. After comparing with practical industrial data, the obtained result shows that the KPSONN is feasible and effective in soft-sensing of acrylonitrile yield