The modeling and knowledge acquiring for the forecast system of water atomized alloy powder´s quality

The forecast system of water atomized alloy powder´s quality is an incomplete information system because of its incomplete original data. In view of the incomplete system, based on the analysis of variable rough set and set pair analysis, a new kind of generalized rough set based on set pair situation tolerance relation was proposed in this paper, and was used in the system modeling. By regulating and controlling the parameter ¿ to promise veracity and flexibility when marking up the productive condition attributes. Based on the proposed model, a cupidity algorithm of attribute significance was introduced and used to realize the productive condition attributes reduction and acquire the relative knowledge. The proposed model and algorithm were applied in the production and it shows the acquired knowledge with high rationality and validity can truly be to optimize the production process.