Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (3): 775-779.DOI: 10.11772/j.issn.1001-9081.2014.03.0775

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Thermal comfort prediction model based on improved particle swarm optimization-back propagation neural network

ZHANG Ling1,WANG Ling1,WU Tong1,2   

  1. 1. College of Electrical and Information Engineering, Hunan University, Changsha Hunan 410082, China;
    2. No.63893 Troops, Luoyang Henan 471000, China
  • Received:2013-07-25 Revised:2013-09-17 Online:2014-03-01 Published:2014-04-01
  • Contact: ZHANG Ling

基于改进的粒子群算法优化反向传播神经网络的热舒适度预测模型

张玲1,王玲1,吴桐1,2   

  1. 1. 湖南大学 电气与信息工程学院,长沙410082
    2. 63893部队,河南 洛阳471000
  • 通讯作者: 张玲
  • 作者简介:张玲(1986-),女,湖南湘潭人,硕士研究生,CCF会员,主要研究方向:神经网络、智能控制;王玲(1962-),女,湖南长沙人,教授,博士,主要研究方向:现代通信与网络、智能控制;吴桐(1987-),男,江苏南通人,助理工程师,硕士研究生,主要研究方向:机器学习、数字图像处理。

Abstract:

Aiming at the problem that thermal comfort prediction, which is a complicated nonlinear process, can not be applied to real-time control of air conditioning directly, this paper proposed a thermal comfort prediction model based on the improved Particle Swarm Optimization-Back Propagation (PSO-BP) neural network algorithm. By using PSO algorithm to optimize initial weights and thresholds of BP neural network, the problem that traditional BP algorithm converges slowly and is sensitive to the initial value of the network was improved in this prediction model. Meanwhile, for the standard PSO algorithm prone to premature convergence, weak local search capabilities and other shortcomings, this paper put forward some improvement strategies to further enhance the PSO-BP neural network capabilities. The experimental results show that, the thermal comfort prediction model based on the improved PSO-BP neural network algorithm has faster algorithm converges and higher prediction accuracy than the traditional BP model and standard PSO-BP model.

Key words: thermal comfort, prediction, Back Propagation (BP) neural network, Particle Swarm Optimization (PSO), model

摘要:

针对热舒适度预测是一个复杂的非线性过程,不便于空调的实时控制应用的问题,提出一种基于改进的粒子群优化(PSO)算法优化反向传播(BP)神经网络的热舒适度预测模型。这一预测模型通过采用PSO算法优化BP神经网络的初始权值和阈值,改善了传统BP算法收敛速度慢及对网络初始值敏感的问题。同时,针对标准PSO算法易出现早熟收敛、局部寻优能力弱等缺点,提出了相应改进策略,进一步提高了PSO优化BP神经网络的能力。实验结果表明:与传统BP模型和标准PSO-BP模型相比,基于改进的PSO-BP算法的热舒适度预测模型具有更高的预测精度和更快的收敛速度。

关键词: 热舒适度, 预测, 反向传播神经网络, 粒子群算法, 模型

CLC Number: