基于SVR的智能建筑火灾预警模型设计
|
摘要
火灾信息处理算法的有效性直接决定着建筑火灾自动预警系统的可靠性,开发新型智能火灾预警算法是目前建筑火灾探测预警领域研究的热点之一.针对现有火灾预警算法的不足,研究设计提出一种基于支持向量回归机(SVR)的智能建筑火灾预警算法.为了验证该算法在多传感器复合式建筑火灾预警系统信息处理中的可靠性与优越性,以普通火灾和欧洲试验火历史数据为例,通过Matlab模拟仿真,进行实证分析,并将预警结果与BP神经网络预警结果进行对比分析.研究成果可为新型建筑火灾自动预警系统的设计提供科学的依据.
The effectiveness of the fire information processing algorithm directly determines the reliability of the building auto-fire warning system,so developing new fire pre-warning algorithm is one of the hotspot in the field of building fire detection and pre-warning research.Aiming at the deficiencies of the existing fire warning algorithm,an intelligent building fire warning algorithm based on the support vector regression machine(SVR) is designed and proposed in the study.In order to verify the information processing reliability and superiority of the algorithm in the composite building fire pre-warning system,with the ordinary fire history data and the European standard test fire history data as example,to make empirical analysis through Matlab simulation,and the pre-warning results were compared with the BP neural network pre-warning results.The research results can provide a scientific basis for the design of new building auto-fire pre-warning system.
The effectiveness of the fire information processing algorithm directly determines the reliability of the building auto-fire warning system,so developing new fire pre-warning algorithm is one of the hotspot in the field of building fire detection and pre-warning research.Aiming at the deficiencies of the existing fire warning algorithm,an intelligent building fire warning algorithm based on the support vector regression machine(SVR) is designed and proposed in the study.In order to verify the information processing reliability and superiority of the algorithm in the composite building fire pre-warning system,with the ordinary fire history data and the European standard test fire history data as example,to make empirical analysis through Matlab simulation,and the pre-warning results were compared with the BP neural network pre-warning results.The research results can provide a scientific basis for the design of new building auto-fire pre-warning system.
引文
[1]Adam C,Adam B,Cecilia A E,Jose T.Fire safety design for tall buildings[J].Procedia Engineering,2013,62:169-181.
[2]厉剑.火灾探测信号处理算法及其性能评估方法研究[D]:[博士学位论文].大连:大连理工大学,2005.
[3]Grosshandler W.Toward the development of a universal fire emulator/detector evaluator[J].Fire Safety Journal,2007,29:113-128.
[4]Klose J.Analysis,synthesis and simulation of signals as a tool for the test of automatic fire detection systems[J].Fire Safety Journal,1991,17(6):499-518.
[5]冯勇.感烟感温复合探测器[D]:[硕士学位论文].合肥:合肥工业大学,2006.
[6]周晓琳.基于神经网络的多传感器数据融合火灾预警系统研究[D]:[硕士学位论文].长春:长春理工大学,2012.
[7]CHEN Y H.Reliability analysis of a fire alarm system[J].Procedia Engineering,2011,24:731-736.
[8]Okayama Y.A primitive study of a fire detection method controlled by artificial neural net[J].Applied Science and Technology,2011,38(5):40-45.
[9]王殊,窦征.火灾探测及其信号处理[M].武汉:华中理工大学出版社,2006.
[10]张键.基于神经网络算法的火灾探测系统的研究[J].数字技术与应用,2013,10:130-132.
[11]汤群芳基于模糊神经网络的火灾数据处理方法的研究[D].[硕士学位论文].长沙:湖南大学,2011.
[12]Cestari L A,Worrell C,Milke JA.Advanced fire Detection Algorithm Using Data from the Home Smoke Detector Project[J].Fire Safety Journal,2005,40:1-28.
[13]薛源,吴建国.基于SVR算法的环模制粒机输出预测[J].工业控制计算机,2013,26(12):56-58.
[14]Hsu S H,Chih T C,Hsu K C.A two-stage architecture for stock price forecasting by integrating selforganizing map and support vector regression[J].Expert Systems with Applications,2009,36(4):7947-7951.
[15]张立宁,张奇,安晶.基于SVR的高层建筑复合式火灾预警系统设计[J].安全与环境工程,2015,22(1):140-143.
[16]胡兆杰.基于BP神经网络和证据理论融合的火灾探测信息处理[D]:[硕士学位论文].天津:天津理工大学,2013.
[17]高强.基于模糊神经网络火灾智能报警系统的研究[D]:[硕士学位论文].沈阳:沈阳航空航天大学,2007
[2]厉剑.火灾探测信号处理算法及其性能评估方法研究[D]:[博士学位论文].大连:大连理工大学,2005.
[3]Grosshandler W.Toward the development of a universal fire emulator/detector evaluator[J].Fire Safety Journal,2007,29:113-128.
[4]Klose J.Analysis,synthesis and simulation of signals as a tool for the test of automatic fire detection systems[J].Fire Safety Journal,1991,17(6):499-518.
[5]冯勇.感烟感温复合探测器[D]:[硕士学位论文].合肥:合肥工业大学,2006.
[6]周晓琳.基于神经网络的多传感器数据融合火灾预警系统研究[D]:[硕士学位论文].长春:长春理工大学,2012.
[7]CHEN Y H.Reliability analysis of a fire alarm system[J].Procedia Engineering,2011,24:731-736.
[8]Okayama Y.A primitive study of a fire detection method controlled by artificial neural net[J].Applied Science and Technology,2011,38(5):40-45.
[9]王殊,窦征.火灾探测及其信号处理[M].武汉:华中理工大学出版社,2006.
[10]张键.基于神经网络算法的火灾探测系统的研究[J].数字技术与应用,2013,10:130-132.
[11]汤群芳基于模糊神经网络的火灾数据处理方法的研究[D].[硕士学位论文].长沙:湖南大学,2011.
[12]Cestari L A,Worrell C,Milke JA.Advanced fire Detection Algorithm Using Data from the Home Smoke Detector Project[J].Fire Safety Journal,2005,40:1-28.
[13]薛源,吴建国.基于SVR算法的环模制粒机输出预测[J].工业控制计算机,2013,26(12):56-58.
[14]Hsu S H,Chih T C,Hsu K C.A two-stage architecture for stock price forecasting by integrating selforganizing map and support vector regression[J].Expert Systems with Applications,2009,36(4):7947-7951.
[15]张立宁,张奇,安晶.基于SVR的高层建筑复合式火灾预警系统设计[J].安全与环境工程,2015,22(1):140-143.
[16]胡兆杰.基于BP神经网络和证据理论融合的火灾探测信息处理[D]:[硕士学位论文].天津:天津理工大学,2013.
[17]高强.基于模糊神经网络火灾智能报警系统的研究[D]:[硕士学位论文].沈阳:沈阳航空航天大学,2007