【作者】 程义菊；

【导师】 宗群；

【作者基本信息】 天津大学 ， 控制理论与控制工程， 2004， 硕士

【摘要】 本论文主要研究了电梯交通流的预测方法。提出了将ARMA（自回归滑动平均模型）数学模型预测法与基于径向基函数的神经网络相结合的混合预测方法。首先分析电梯交通流样本数据，判断电梯交通流可以根据ARMA预测模型进行预测，分析ARMA模型预测方法预测电梯交通流的特点，通过比较RBF神经网络和BP神经网络，由于RBF网络具有良好的逼近任意非线性映射和处理系统内在的难以解析表达的规律性能力，并且具有较快的学习收敛速度，明显优于BP网络和其它方法，因此选用基于径向基函数的RBF的神经网络方法对电梯交通流预测进行改进。提出选用ARMA与RBF神经网络结合的混合预测方法。建立电梯交通流混合预测模型，根据交通流样本数据确定混合模型的参数，包括ARMA模型的参数和RBF神经网络的参数,建立电梯交通流的混合预测模型，并对模型进行学习训练。分析两种预测方法相结合的混合预测方法的预测效果，与ARMA预测和其他方法的预测效果进行比较，分别计算它们的预测误差值，比较混合预测法与其他方法预测的精度和优劣。通过VC编程和数据库技术实现电梯交通流的预测，通过输入的交通流样本数据，预测未来时刻的电梯交通流状况，得出混合预测模型预测方法具有明显的优势。通过对混合预测方法的仿真试验，比较预测值与实际值来验证预测效果的拟和效果，说明混合预测方法可以较好的预测电梯交通流。建立电梯交通系统预测模型后，由于ARMA模型具有较强的预测能力和RBF神经网络具有较强的学习能力，对于电梯交通流的随机变化具有很好的适应能力。在论文的最后，给出了混合预测模型与其他预测方法的比较，可以得出混合预测模型预测的效果较好。无论是计算预测误差还是进行预测的速度，混合预测方法都较好。更多还原

【Abstract】 This thesis is mainly about the research in forecast approach for elevator traffic system. The hybrid forecast approach of combining the ARMA mathematical model forecast approach and Radial Basis Function neural network approach is proposed. The elevator traffic flow sample data is analyzed first, according to which the parameters and orders of the ARMA forecast model could be fixed on, and the forecast model for the elevator traffic can be built. So we can use the model to forecast the elevator traffic. Then the author analyzed the defects of ARMA model forecast approach in forecast the elevator traffic and compared the RBF and BP neural network. Because the RBF network is well capable of approaching arbitrary non-linear mapping and processing the inner-system principle which is hard to be expressed in resolution and has a faster study convergence speed. It is much superior to BP network and other approaches and the RBF network approach is selected to improve the elevator traffic flow forecast. The effect of the hybrid forecast approach and those of the single ARMA forecast and RBF network forecast are compared. Calculating their forecast error respectively, we can compare their precision and performance. By programming in VC and using database technology, we can realize the forecast for the elevator traffic flow. So with the traffic flow sample data, we can forecast the elevator traffic flow in the future. The result is that the hybrid approach outperforms other approaches outlined in the thesis. The similarity between the expected value and real value in simulation can show the effect of forecast. The elevator traffic system adopts the hybrid approach. Because the ARMA model has better forecast ability while the RBF network has better study ability, so the approach is quite adaptive to the random variance in elevator traffic flow.After building the forecast model for elevator traffic system, the model can be used to forecast the elevator traffic flow. Further we can select different forecast approach, from which come out different results. At the end of the thesis, the comparison of the hybrid and single forecast approach is given, and the hybrid approach is much superior, no matter whether to calculate the forecast error or the forecast speed.更多还原