基于神经网络的围护结构动态热特性——状态空间模型辨识方法的研究(英文)

A Neural-Network-Based Identification Method to Determine the State Space Model of Dynamic Thermal Behavior of Building Envelopes

本文首次提出了基于神经网络的围护结构动态热特性——状态空间模型的辨识方法.由实验测得的围护结构热力系统的输入输出数据组成学习样本训练多层神经网络,产生系统 Markov 参数.用本征系统实现算法得到系统的最小阶状态空间表示.用自适应学习算法大大缩短了网络的训练时间.结果表明:该方法编程简单,有很好的辨识精度和抗干扰性能.

A neural-network-based identification method to determine the state space model of dynamic thermal behavior of building envelope is presented first in this paper.A multilayered neural network is trained by the samples constructed from the experimentally measured input and output data of wall's thermodynamic system,and then the Markov parameters are produced,which are utilized to realize the reduced state space model expression by eigensystem realization algorithm.The training time is greatly reduced by the adaptive learning algorithm.The results show that this method has some advantages in programming and computational simplicity,very good properties of noise rejection and improved accuracy of the results.