Spatial Interpolation Using Neural Fuzzy Technique
Spatial interpolation is an important feature of a Geographic Information System, which is the procedure used to estimate values at unknown locations within the area covered by existing observations. This paper constructs Juzzy rule bases with the aid of a Self-organising Map (SOM) and Backpropagation Neural Networks (BPNNs). These juzzy rule bases are then used to perform spatial interpolation. A case based on the 467 rainfall data in Switzerland is used to test the neural fuzzy technique. The SOM is first used to classify the data. After classification, BPNNs are then use to learn the generalization characteristics from the data within each cluster. Fuzzy rules for each cluster are then extracted. The fuzzy rules base are then used for rainfall prediction.
Kok Wai Wong Tam(a)s D. Gedeon Chun Che Fung Patrick M. Wong
School of Information Technology Murdoch University Western Australia School of Electrical and Computer Engineering Curtin University Western Australia School of Petroleum Engineering University of New South Wales Sydney, Australia
国际会议
8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)
上海
英文
355-359
2001-11-14（万方平台首次上网日期，不代表论文的发表时间）