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To effectively predict the permeability index of smelting process in the imperial smelting furnace,anintelligent prediction model is proposed.It integrates the case-based reasoning(CBR)with adaptive par-ticle swarm optimization(PSO).The number of nearest neighbors and the weighted features vector areoptimized online using the adaptive PSOto improve the prediction accuracy of CBR.The adaptive inertiaweight and mutation operation are used to overcome the premature convergence of the PSO.The proposedmethod is validated a compared with the basic weighted CBR.The results show that the proposed modelhas higher prediction accuracy and better performance than the basic CBR model.
The effective index of smelting furnace in the imperial smelting furnace, anintelligent prediction model is proposed. It integrates the case-based reasoning (CBR) with adaptive par-ticle swarm optimization (PSO). The number of nearest neighbors and the weighted features vector areoptimized online using the adaptive PSO to improve the prediction accuracy of CBR.The adaptive inertiaweight and mutation operation are used to overcome the premature convergence of the PSO.The proposedmethod is validated a comparable with the basic weighted CBR.The results show that the proposed modelhas higher prediction accuracy and better performance than the basic CBR model.