基于广义遗传优化的RBF算法在铁水脱硫中的应用

Application of RBF Algorithm Based on Generalized Genetic Optimization in Prediction Model for Molten Iron Desulfuration

铁水脱硫过程是一个非常复杂的多元非线性反应过程,在研究了基本遗传算法和RBF算法的基础上,提出了基于广义遗传优化的RBF算法。并说明了基于广义遗传优化的RBF算法在某钢厂铁水脱硫预报模型中的应用。该算法不仅克服了RBF中心个数选择的随机性,而且较好地解决了遗传算法时间复杂度高的缺点。通过对基于广义遗传优化的RBF算法与RBF算法的仿真比较,进一步阐明了该算法在铁水脱硫预报模型中的有效性和精确性。并且现场实验也表明,该算法能够达到终点命中率在 85%以上,这说明了该算法的工程实用性。

Desulfuration process is a very sophisticate reaction which is not only diverse but also non-line. A RBF algorithm based on generalized genetic optimization is proposed after studying the standard genetic and RBF algorithm. The authors also introduce its application in prediction Model for molten Iron Desulfuration. The algorithm perfectly resolve the problem of random selection of RBF cluster center number. Furthermore, it also reduces the time which GA uses. Comparison between the simulation results of RBF and RBF algorithm Based on GGA optimization further proves the efficiency and precision of its application in Prediction Model for Molten Iron Desulfuration. Finally the result of the test shows that after adopting the algorithm, the end-point hitting ratio can reach eighty-five percent. This indicate the algorithm has the engineering practicability.