PSO优化ELM在火灾探测中的应用

消防科学与技术 ›› 2022, Vol. 41 ›› Issue (1): 91-94.

PSO优化ELM在火灾探测中的应用

郑皓天1，张树川1，朱俊奇2

（1.安徽理工大学安全科学与工程学院，安徽淮南 232001；2.安徽理工大学经济与管理学院，安徽淮南 232001）

出版日期:2022-01-15 发布日期:2022-01-15
作者简介:郑皓天(1998-)，男，河南永城人，安徽理工大学安全科学与工程学院硕士研究生，主要从事防火工程、消防安全管理方面的研究，安徽省淮南市山南新区泰丰大街168号，232001。

Application of PSO optimized ELM in fire detection

ZHENG Hao-tian1, ZHANG Shu-chuan1,ZHU Jun-qi2

(1.?? College of Safety Science and Engineering, Anhui University of Science and Technology, Anhui Huainan 232001, China; 2. College of Economics and Management, Anhui University of Science and Technology, Anhui Huainan 232001, China)

Online:2022-01-15 Published:2022-01-15

摘要/Abstract

关键词: 火灾探测；粒子群算法；BP神经网络；GA-BP；极限学习机

Abstract: In order to improve the accuracy of fire detection and avoid the standard ELM falling into local optimization, this paper constructs a fire detection model based on the fire characteristic value CO concentration, smoke concentration and temperature, and optimizes the ELM input layer and the hidden layer weight and bias through PSO. The best optimal value is used to train the extreme learning machine network, and the trained network is used to predict the test samples and verify the effectiveness of the method. The study shows that the mean square root error (RMSE) of PSO-ELM is 1.403%, the average absolute error (MAE) is 1.055%, and the average absolute percentage error (MAPE) is 1.183%. Compared with BP, GA-BP and ELM models, the algorithm accuracy and generalization ability are obviously improved. At the same time, PSO-ELM model training speed is faster, can improve the fire detection ability more efficiently.

Key words: fire detection; particle swarm algorithm; BP neural network; GA-BP; extreme learning machine

郑皓天1，张树川1，朱俊奇2. PSO优化ELM在火灾探测中的应用[J]. 消防科学与技术, 2022, 41(1): 91-94.

ZHENG Hao-tian1, ZHANG Shu-chuan1,ZHU Jun-qi2. Application of PSO optimized ELM in fire detection[J]. Fire Science and Technology, 2022, 41(1): 91-94.