动态模糊神经网络论文：基于D-FNN加热炉钢温建模及优化应用研究.doc

动态模糊神经网络论文：基于D-FNN的加热炉钢温建模与优化研究 【中文摘要】加热炉是轧制生产环节中重要的热工设备,合理控制钢坯出炉温度不仅能保证轧制质量而且能够降低加热炉燃料的消耗,特别是在能源日益紧张的今天,建立有效的加热炉钢温模型,推算钢坯的出炉温度具有重要的意义。本文以蓄热式步进梁加热炉为,简单介绍了蓄热式加热炉概况和智能算法。根据对以往采用的智能建模方法的研究和分析,本文提出了一种基于动态模糊神经网络(D-FNN)的钢坯温度预测模型。本文基于加热炉的生产数据,对如何应用和优化D-FNN建立钢坯温度预测模型进行了研究。首先,本文用动态模糊神经网络建立钢坯温度预测模型,并且通过仿真结果验证动态模糊神经网络适用于建立加热炉的钢温预测模型。其次,本文用扩展的卡尔曼滤波(EKF)对D-FNN的前提参数进行调整,用线性最小二乘(LLS)调整D-FNN的结果参数。用参数优化后的D-FNN建立钢坯温度预测模型。为了进一步提高模型的精度和辨识的速度,并且通过比较建立的钢温预测模型的仿真结果发现,结果参数的优化方法有待改进。本文尝试用粒子群优化算法(PSO)代替LLS来优化结果参数。再次,从仿真结果发现PSO算法在优化后期的迭代过程中收敛速度下降,且易于陷入局部极限值。本文尝试用人工免疫克隆算法来改进PSO算法,提出了一种用免疫记忆多克隆选择算法(IMMCSA)改进PSO的优化方法。用改进后的IMMCSA-PSO来调整结果参数,用改进后的网络建立钢坯温度预测模型。仿真结果证明,基于EKF和IMMCSA-PSO优化参数后所建立的钢温预测模型的仿真实验结果较好,误差已经缩小到期望的范围内。根据预测的钢坯温度操作人员就能判断加热过程是否异常,以便采取有效控制和处理来保证钢坯质量。因此,改进参数优化方法的D-FNN模型不仅具有理论研究意义,而且具有实际应用的价值。 【英文摘要】Reheating furnace is an important thermal technology equipment in the process of billet rolling. It can assure the rolling quality and reduce energy consuming in heating furnace if the billet’s temperature is control reasonably. What抯 more, the shortage of energy sources is seriours nowadays. Therefore, it is significant to establish a valid model of the furnace, calculateing the outlet strip temperature.In this paper, based on walking beam regenerative reheating furnace system, there are a brief overview of regenerative reheating furnace and intelligent algorithm. In this paper D-FNN(Dynamic Fuzzy Neural Networks) model proposed is used to the billet temperature forecasting. In this paper, accoding to reheating furnace抯 production data, how to apply and optimize D-FNN to build predictiong model of billet temperature had been studied. Firstly, the model of billet抯 temperature prediction with D-FNN has proposed. And the D-FNN for modelling the billet temperature prediction has been verified through simulation results. Secondly, the main parameters of D-FNN has been optimized. This paper used the EKF(Extended Kalman Filter) to optimize the parameters of the premise, and used the LLS(Linea