摘要
根据改进的BP神经网络基本原理,建立了烧结砖的神经网络强度预测模型.利用试验数据训练神经网络,通过工程实例,对训练过的神经网络进行了测试,并将用神经网络方法获得的结果与用传统数学回归模型计算的结果进行了对比.结果表明,该神经网络方法所得的预测值优于传统方法.
A method of artificial neural network was presented to forecast the compressive strength of clay brick in view of the questions existing in the ultrasonic and rebound strength testing method. A relative forecasting model was established base on the principles of the improved BP neural network. The neural network was trained by the test data, and a case study of a real engineering was tested. The results show that the forecasting results obtained from the neutral network method is better than that from the traditional mathematic model.
出处
《建筑材料学报》
EI
CAS
CSCD
2005年第3期284-288,共5页
Journal of Building Materials
基金
国家科技攻关计划资助项目(2002BA806B4)
关键词
超声回弹综合法
抗压强度
回归模型
BP神经网络
combination of ultrasonic and rebound method
compressive strength
regression model
BP neural network
