【摘要】 提出了一种用于预报转炉炼钢终点磷含量的智能方法,在该方法中,采用模糊推理和遗传算法,其中模糊推理用于估算转炉熔池的磷含量,而模糊推理模型中的各个系数则由遗传算法辨识与优化。为了提高熔池磷的估算精度,同时还设计了一种神经网络以补偿来自模糊推理过程的误差。仿真结果表明了该方法的有效性更多还原

【Abstract】 An intelligent model was introduced to estimate the bath endpoint phosphorus content for basic oxygen furnace (BOF) steelmaking. In the model, the fuzzy inference and genetic algorithms (GA) were used. The BOF endpoint [P] content was estimated by fuzzy inference model, and the parameters for the fuzzy model were optimized by genetic algorithms. For improving the precision of the endpoint estimation, an artificial neural network (ANN) was designed to compensate for the fuzzy inference error. And the simulation results show the validity of this method.更多还原