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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Zhong-yu Chen Desai, M. Xiao-Ping Zhang |
| Copyright Year | 1997 |
| Description | Author affiliation: Div. of Eng., Texas Univ., San Antonio, TX, USA (Zhong-yu Chen) |
| Abstract | Artificial neural network has been, used as a powerful tool for pattern classification. However, it is difficult to train when the data exhibit non-sparse or overlapping pattern classes which is often the case in practical applications. In this paper, we introduce the feedforward neural network with the hidden layer consisting of multilevel neurons. The convergence property of one-layer neural network with multilevel neurons is proved. The new feedforward model is inherently capable of fuzzy pattern classification of non-sparse or overlapping pattern classes. As an application, we apply the network for the classification of LANDSAT TM data. The results show that this approach produces better results compared with conventional neural networks. |
| Starting Page | 653 |
| Ending Page | 656 |
| File Size | 710916 |
| Page Count | 4 |
| File Format | |
| ISBN | 0818681837 |
| DOI | 10.1109/ICIP.1997.638580 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1997-10-26 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Neural networks Feedforward neural networks Neurons Artificial neural networks Pattern classification Convergence Satellites Remote sensing |
| Content Type | Text |
| Resource Type | Article |
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