基于径向基函数神经网络的高层建筑结构选型  

Choice of structural styles for tall building based on radial basis function neural network

作　　者：王全凤[1] 郑浩[1] 

机构地区：[1]华侨大学土木工程学院,福建泉州362021

出　　处：《四川建筑科学研究》2010年第5期18-21,共4页Sichuan Building Science

摘　　要：提出了应用径向基函数神经网络进行高层结构体系的选型,它充分运用了神经网络高度的非线性、高度的容错性和鲁棒性、自学习、实时处理等特点。研究表明,径向基函数神经网络运算速度较普通BP算法快103～104倍,并且精度高,可以高效、高质地进行高层建筑结构的选型。In the early stage of the design process,the design of tall building is a complex work. It needs various knowledge and professional experience for the structural design. A way concerned about the choice of structural styles is put forward based on radial basis function neural network （ RBFNN） in this paper. The qualities of the RBFNN,high-nonlinear,high-permissibility of error and high-robustness,self-adaptability,online work,and so on,are adequately used in the research. And,it is concluded that RBFNN runningspeed is 103～ 104 times faster than traditional BP neural network＇s algorithm. From the research,the method based on RBFNN can solve the problem on choice of structural styles effectively and quickly.

关 键 词：高层建筑 结构选型 人工神经网络 径向基函数神经网络 

分 类 号：TU973[建筑科学—结构工程]