基于神经网络的大跨度屋盖非高斯风压场模拟方法
孙芳锦;张爱社
【期刊名称】《郑州大学学报（工学版）》 【年(卷),期】2011(032)004
【摘要】采用径向基函数神经网络(Radical Basis Function Neutral Networks,简称RBF神经网络)来模拟大跨度结构的非高斯风压场.根据某大跨度结构的形式特点,将结构风场看成是屋面位置和时间的函数,将风压场分解为一系列径向基函数.再利用单调非线性无记忆转换映射和RBF中获得的风场函数定义向量过程,从而将非高斯场的模拟转换为互相关高斯过程的模拟.将RBF神经网络应用于一大跨度屋盖的非高斯场模拟,得到结构上非高斯风压场的分布.结果对比表明,RBF神经网络模拟非高斯风压场具有较高的准确性.该方法可直接利用RBF神经网络的输出结果,避免推导高斯过程和非高斯过程的关系式,因此具有较高的效率.RBF神经网络模拟非高斯风压场在准确性和效率上均具有显著优势.%Radical basis function neural networks ( RBF neural networks for short) are adopted to simulate numerically non-Gaussian wind field of large-span roofs. According to properties of a large-span roof, the wind field is considered as the function of position and time, decomposed into a series of radical basis functions. And monotonic nonlinear memoryless transformation mapping and wind field function obtained from RBF neural networks are combined to define a transformation vector process, with which non-Gaussian process is transformed to Gaussian process for simulation. The proposed RBF neural networks are
基于神经网络的大跨度屋盖非高斯风压场模拟方法
