受限空间细水雾作用下烟气温度变化规律研究 |
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引用本文: | 房玉东,苏国锋,林霖,廖光煊.受限空间细水雾作用下烟气温度变化规律研究[J].自然科学进展,2008,18(4):475-480. |
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作者姓名: | 房玉东 苏国锋 林霖 廖光煊 |
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作者单位: | 1. 清华大学公共安全研究中心,北京,100084 2. 中国科学技术大学火灾科学国家重点实验室,合肥,230026 |
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摘 要: | 利用热电偶测量细水雾作用下烟气层不同高度温度,研究雾滴粒径、雾通量和喷头与火源的水平距离等因素对平均细水雾降温速率(V)的影响规律.揭示了细水雾抑制火灾烟气温度的主导机理.利用实验数据推导V与雾通量之间的数学关系,建立V空间分布的三维数学模型,为细水雾技术用于火灾烟气抑制提供理论基础和必要的设计参数.
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关 键 词: | 细水雾 烟气 温度 |
修稿时间: | 2007年1月24日 |
Quantum-inspired evolutionary tuning of SVM parameters |
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Authors: | Zhiyong Luo Ping Wang Yinguo Li Wenfeng Zhang Wei Tang Min Xiang |
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Abstract: | Common used parameters selection method for support vector machines (SVM) is cross-validation, which needs a long-time complicated calculation. In this paper, a novel regularization parameter and kernel parameter tuning approach of SVM is presented based on quantum-inspired evolutionary algorithm (QEA). QEA with quantum chromosome and quantum mutation has better global search capacity. The parameters of least squares support vector machines (LS-SVM) can be adjusted using quantum-inspired evolutionary optimization. Classification and function estimation are studied using LS-SVM with wavelet kernel and Gaussian kernel. The simulation results show that the proposed approach can effectively tune the parameters of LS-SVM , and the improved LS-SVM with wavelet kernel can provide better precision. |
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Keywords: | quantum-inspired evolutionary algorithm (QEA) parameters tuning support vector machines (SVM) least squares support vector machines (LS-SVM) |
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