Study of the agent quantum control model in hot metal desulphurization process based on Rough Set and GA-RBF nerve network

In view of low precision and low auto-adapted ability in traditional desulphurization control model, according to the mechanism of hot metal desulphurization process, the RBF nerve network desulphurization agent quantum model based on rough set and genetic algorithm is introduced. This model uses rough set to clean modeling data, uses genetic algorithm to select RBF network structure, and then introduces generalization error to the network training process. The emulate contrast shows the mathematical model can suffice for the requirement of hot metal desulphurization control process.