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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Shu-Hua Zhang |
| Copyright Year | 2012 |
| Abstract | A model of probabilistic neural network (PNN) to classify the loess according to its collapsibility is suggested in this paper, in which five physical property indexes such as water content, dry density, void ratio, saturation degree and plastic liquid are taken as input neural cells and the output neural cell is coefficient of collapsibility. 76 groups of sample to be trained under different smoothing parameter as so to get an optimal parameter and obtained a suitable network with a good performance. The prediction result from PNN after training has a higher accuracy with 90% in 20 group testing samples, which is better than the accuracy predicted by RBF model. The result shows that the PNN is an effective classification method for collapsibility of loess and it is likely to implement in practice. |
| Starting Page | 307 |
| Ending Page | 310 |
| File Size | 270393 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781467307215 |
| e-ISBN | 9780769547190 |
| DOI | 10.1109/CSSS.2012.84 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-08-11 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training Smoothing methods Probabilistic neural network Neural networks Support vector machine classification RBF Soil Probabilistic logic Indexes Collapsibility classification |
| Content Type | Text |
| Resource Type | Article |
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