Rough neural network of variable precision
In this paper, a new method is described to construct rough neural networks. On the base of rough set model, we present a method to develop rough neural network of variable precision and train it using Levenberg–Marquart algorithm. The method is particularly attractive because it combines the advant...
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| Veröffentlicht in: | Neural processing letters 2004-02, Vol.19 (1), p.73-87 |
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| container_title | Neural processing letters |
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| creator | Liu, Hongjian Tuo, Hongya Liu, Yuncai |
| description | In this paper, a new method is described to construct rough neural networks. On the base of rough set model, we present a method to develop rough neural network of variable precision and train it using Levenberg–Marquart algorithm. The method is particularly attractive because it combines the advantages of both rough logic networks and neural networks. In our system, weak generalization in rough sets theory and complexity in neural network are avoided while anti-jamming performance is highly improved and the network structure is also simplified. In experiments, the network is applied to classification of remote sensing images. The results show that our method is more effective and successful than application of rough sets and neural network separately. |
| doi_str_mv | 10.1023/B:NEPL.0000016851.47914.40 |
| format | Article |
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| ispartof | Neural processing letters, 2004-02, Vol.19 (1), p.73-87 |
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| subjects | Algorithms Applied sciences Artificial intelligence Computer science control theory systems Connectionism. Neural networks Exact sciences and technology Jamming performance Neural networks Remote sensing Rough set models |
| title | Rough neural network of variable precision |
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