基于改进Elman神经网络的徽派古建筑寿命预测

doi:10.3969/j.issn.0253-2778.2017.10.003

中国科学技术大学学报 ›› 2017, Vol. 47 ›› Issue (10): 817-822.DOI: 10.3969/j.issn.0253-2778.2017.10.003

基于改进Elman神经网络的徽派古建筑寿命预测

张广斌，张润梅

1.安徽建筑大学电子与信息工程学院，安徽合肥 230601;
2.安徽建筑大学机械与电气工程学院，安徽合肥 230601

收稿日期:2017-05-19 修回日期:2016-06-23 出版日期:2017-10-31 发布日期:2017-10-31
通讯作者: 张广斌
作者简介:张广斌(通讯作者)，男，1977，博士/副教授. 研究方向: 古建筑保护/神经网络的应用. E-mail: gbzhangcn@qq.com
基金资助:
十二五国家科技支撑计划（2012BAJ08B00）,安徽质量工程项目(2014zdjy091)，安徽建筑大学博士启动基金，易海人才工程资助.

Life span prediction of Huizhou architecture based on improved Elman neural network

ZHANG Guangbin, ZHANG Runmei

1. School of Electronics and Information Engineering, Anhui Jianzhu University, Hefei 230601, China；
2. School of Mechanical and Electrical Engineering, Anhui Jianzhu University, Hefei, 230601, China）

Received:2017-05-19 Revised:2016-06-23 Online:2017-10-31 Published:2017-10-31

摘要/Abstract

摘要： 徽派建筑是我国四大古建筑流派之一，木构件是徽派建筑的核心.准确预测徽派木构件的寿命，对于古建筑的保护具有重要的意义.目前系统考虑多种因素对木构件寿命共同影响的研究较少,Elman神经网络是一种典型的多层动态递归神经网络，通过存储内部状态使其具备映射动态特性的功能,从而使系统具有适应时变特性的能力，可用于预测木构件复杂的非线性时变系统的建模.针对基本的Elman神经网络存在训练速度慢、容易陷入局部极小值的特点，使用带有自适应变异算子的粒子群优化算法对基本的Elman 神经网络进行改进，优化网络中各层之间的连接权值，提高学习速度，并在全局范围内寻找最优解.仿真结果表明,改进后的网络能较准确地拟合训练值，并进行有效预测，能够较好应用于徽派古建筑寿命预测.

关键词: Elman神经网络, 粒子群, 木构件, 寿命预测

Abstract: Huizhou architecture comprises one of the four ancient architectural schools in China, with wood components being its core. The accurate prediction of Huizhou architectures wood life is of great significance for the protection of ancient buildings. At present, there are few studies have been conducted on the influence of various factors on the service life of the wood components. Elman neural network is typical multi-layer dynamic recurrent neural network, which has the function of mapping dynamic characteristics by storing internal state. This gives the network the ability to adapt to time-varying characteristics, which can be used to predict the complex nonlinear time-varying system. The basic Elman neural network has the characteristics of slow training speed and the tendency to fall into local minimums. Therefore the particle swarm optimization algorithm with adaptive mutation operator is used to improve the basic Elman neural network. The algorithm optimizes the weights of each layer in the network, improves the learning speed, and finds the optimal solution in the global range. The improved network can fit the training value more accurately and can effectively predict the test value. The simulation results show that the network structure can be well applied to the life span prediction of Huizhou architecture.

Key words: Elman neural network, particle swarm optimization;wood component;life prediction

中图分类号:

TP391

张广斌，张润梅. 基于改进Elman神经网络的徽派古建筑寿命预测[J]. 中国科学技术大学学报, 2017, 47(10): 817-822.

ZHANG Guangbin, ZHANG Runmei. Life span prediction of Huizhou architecture based on improved Elman neural network[J]. Journal of University of Science and Technology of China, 2017, 47(10): 817-822.

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基于改进Elman神经网络的徽派古建筑寿命预测

Life span prediction of Huizhou architecture based on improved Elman neural network

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