CAJ | 학술논문

针对高炉关键异常炉况悬料难以预测的问题，基于D-S证据理论，提出一种综合模糊专家推理和后验概率最小二乘支持向量机的悬料预测方法。首先，结合高炉生产过程和悬料现象，分析悬料形成的内在机理；其次，通过模糊专家推理提取基于专家规则的主观证据，再通过建立后验概率最小二乘支持向量机模型提取基于数据内在客观规律的客观证据；最后，基于D-S证据理论完成主客观证据融合，实现悬料预测。该方法充分利用专家经验和最小二乘支持向量机的自学习能力，能够提高预测精度。仿真结果表明本文提出的方法有效、准确。
침대고로관건이상로황현료난이예측적문제，기우D-S증거이론，제출일충종합모호전가추리화후험개솔최소이승지지향량궤적현료예측방법。수선，결합고로생산과정화현료현상，분석현료형성적내재궤리；기차，통과모호전가추리제취기우전가규칙적주관증거，재통과건립후험개솔최소이승지지향량궤모형제취기우수거내재객관규률적객관증거；최후，기우D-S증거이론완성주객관증거융합，실현현료예측。해방법충분이용전가경험화최소이승지지향량궤적자학습능력，능구제고예측정도。방진결과표명본문제출적방법유효、준학。
Aiming at the difficulty of predicting blast furnace hanging, a prediction method was proposed for the hanging based on the D-S evidence theory and in combination with fuzzy expert inference and a posterior probability least squares support vector machine. Firstly, the causes of hanging are obtained by mechanism analysis in consideration of blast furnace operations and hanging phenomena. Secondly, subjective evidences are extracted by fuzzy expert reasoning, while a posterior probability least squares support vector machine model is developed to extract objective evidences. Finally, in order to predict the hanging precisely, the subjective and objec-tive evidences are fused based on the D-S evidence theory, which makes full use of the expertise and the self-learning ability of the least squares support vector machine. Simulation results illustrate that the proposed method can make accurate prediction of the hanging.