维普期刊资源整合服务平台（V6.5）

维普中文期刊产品整合服务

1篇 您的检索式：作者名="S.Renuga Devi"

选中清除导出查看参考文献查看引证文献引用追踪共1页首页上一页第1页下一页末页 /1 跳转

		题名	作者	年代	出处	被引量
	1	Performance Comparison of Artificial Neural Network Models for Daily Rainfall Prediction显示文摘With an aim to predict rainfall one-day in advance, this paper adopted different neural network models such as feed forward back propagation neural network(BPN), cascade-forward back propagation neural network(CBPN), distributed time delay neural network(DTDNN) and nonlinear autoregressive exogenous network(NARX), and compared their forecasting capabilities. The study deals with two data sets, one containing daily rainfall, temperature and humidity data of Nilgiris and the other containing only daily rainfall data from 14 rain gauge stations located in and around Coonoor(a taluk of Nilgiris). Based on the performance analysis,NARX network outperformed all the other networks. Though there is no major difference in the performances of BPN, CBPN and DTDNN, yet BPN performed considerably well confirming its prediction capabilities. Levenberg Marquardt proved to be the most effective weight updating technique when compared to different gradient descent approaches. Sensitivity analysis was instrumental in identifying the key predictors.	S.Renuga Devi P.Arulmozhivarman C.Venkatesh Pranay Agarwal	2016	International Journal of Automation and computing2016，13，5：	3

返回顶部每页显示：

共1页首页上一页第1页下一页末页 /1 跳转

goolge学术搜索-维普

网站首页 | 关于我们 | 联系我们 | 产品服务 | 客服中心 | 广告服务 | 版权声明 | 网站联盟 | 友情链接 | 售卡网点

版权所有© 渝B2-20050021-1　渝公网安备 50019002500403号　违法和不良信息举报中心

互联网出版许可证新出网证(渝)字10号全国400电话 - 免长途话费