巢湖学院学报Issue(6)：7-11,5.
基于IFOA-RBF算法的混凝土抗压强度预测
ON THE PREDICTION OF CONCRETE COMPRESSIVE STRENGTH BASED ON THE ALGORITHM OF IFOA-RBF
摘要
Abstract
In order to improve the accuracy of predicting the concrete compressive strength,an IFOA-RBF forecasting model for predicting the concrete compressive strength is established through improving the IFOA and optimizing the Spread value of RBF neural network. The model uses the concrete compressive strength data in UCI database as an example, and the simulation testing is carried out by setting the content of cement, blast furnace slag, fly ash, water, water reducer, coarse aggregate and fine aggregate in per cubic concrete and using days of their placement as the network input, and meanwhile the concrete compressive strength value is used as the network output.Then this paper compares the results with the conclusion of the references. The results show that: the optimized RBF network not only embodies the extensive mapping ability, but also significantly improves the network generalization ability. The validity of the IFOA-RBF model in concrete compressive strength prediction is verified.关键词
果蝇优化算法/RBF神经网络/参数优化/混凝土抗压强度Key words
FOA/RBF neural network/parameter optimization/concrete compressive strength分类
信息技术与安全科学引用本文复制引用
徐富强,陶有田..基于IFOA-RBF算法的混凝土抗压强度预测[J].巢湖学院学报,2014,(6):7-11,5.基金项目
安徽高校省级自然科学研究项目 （）
大学数学基础课程教学团队 （）
