Prediction of Grain Yield Based on Spiking Neural Networks Model

摘要：

Grain yield is important in national economy so there is necessary for grain yield prediction. A novel predicting model based on spiking neural networks (SNNs) is presented for this purpose. SNNs are computationally more effective than conventional artificial neural networks. The spiking neurons act as basic elements in which information deliver from one neuron to another in forms of multiple spikes via plenty of synapses. Besides, the corresponding learning mechanism called Spikeprop is also discussed. An example, prediction of. China annual grain yields as our experiment, is used to explain the principle of SNNs based method. Experimental results are demonstrated showing the feasibility and accuracy of our approach.

关键词： piking neural network learning mechanism grain yield prediction artificial neural networks

作者: Lin Yang Teng Zhongjian

作者单位: School of Economics Fujian Normal University Fuzhou, China School of Physics and OptoElectronics Technology Fujian Normal University Fuzhou, China

会议类型: 国际会议

会议名称: 2011 2nd International Conference on Data Storage and Data Engineering(DSDE 2011)(2011年第二届数据存储与数据工程国际会议)

会议地点: 西安

会议语种:英文

页码: 171-174

在线出版日期: 2011-05-13（万方平台首次上网日期，不代表论文的发表时间）