Variable Discretization Precision Rough Logic Neural Network Based on Approximation Area Partition and Its Application toRemote Sensing Image Classification

Zhang Dong-bo; Wang Yao-nan

doi:10.3724/SP.J.1146.2006.00584

Volume 29 Issue 11

Jan. 2011

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Article Navigation > Journal of Electronics & Information Technology > 2007 > 29(11): 2720-2724

Zhang Dong-bo, Wang Yao-nan. Variable Discretization Precision Rough Logic Neural Network Based on Approximation Area Partition and Its Application toRemote Sensing Image Classification[J]. Journal of Electronics & Information Technology, 2007, 29(11): 2720-2724. doi: 10.3724/SP.J.1146.2006.00584

Citation:

Zhang Dong-bo, Wang Yao-nan. Variable Discretization Precision Rough Logic Neural Network Based on Approximation Area Partition and Its Application toRemote Sensing Image Classification[J]. Journal of Electronics & Information Technology, 2007, 29(11): 2720-2724. doi: 10.3724/SP.J.1146.2006.00584

Citation:

Zhang Dong-bo, Wang Yao-nan. Variable Discretization Precision Rough Logic Neural Network Based on Approximation Area Partition and Its Application toRemote Sensing Image Classification[J]. Journal of Electronics & Information Technology, 2007, 29(11): 2720-2724. doi: 10.3724/SP.J.1146.2006.00584

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Variable Discretization Precision Rough Logic Neural Network Based on Approximation Area Partition and Its Application toRemote Sensing Image Classification

doi: 10.3724/SP.J.1146.2006.00584

Zhang Dong-bo^{①②;王耀南},
Wang Yao-nan

Received Date: 2006-05-08
Rev Recd Date: 2006-10-08
Publish Date: 2007-11-19

Abstract

Abstract

A variable discretization precision rough logic neural network is proposed to solve contradiction between network precision and the size of network as well as generalization ability. Based on the approximation area partition, the universe discussed can be partitioned into certain area and possibility area. The important reason of misclassification is the granularity of the possibility area is too coarse. In this work, only possibility area is refined and the precision of the rough logic neural network is improved while the size of network is restrained. In the experiment of the remote sensing image classification about Changbai mountain area, the performance of conventional method is best when the discretization level is 7. The most approximated result is acquired, while less network cost and training time are expended, when this method is used.

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References

Pawlak Z. Rough set theory and its application to data analysis[J].Cybernetics and Systems.1998, 29(9):661-688[2]Pawlak Z. Rough set theory for intelligent industrial applications. Intelligent Processing and Manufacturing of Materials, 1999, IPMM'99,Honolu, 10-15 July, 1999, 1: 37-44.[3]Pawlak Z. Rough logic. Bulletin of the Polish Academy of Sciences, Technical Science, 1987, 35: 253-258.[4]张东波, 王耀南, 易灵芝. 粗集神经网络及其在智能信息处理领域的应用. 控制与决策, 2005, 20(2): 121-126. Zhang Dong-bo, Wang Yao-nan and Yi Ling-zhi. Rough neural network and its application to intelligent information processing. Control and Decision, 2005, 20(2): 121-126.[5]Mohua B, Sushmita M, and Sankar K P. Rough fuzzy MLP: knowledge encoding and classification. IEEE Trans. on Neural Networks, 1998, 9(6): 203-1216.[6]Yu C Y, Wu M H, and Wu M. Combining rough set theory with neural network theory for pattern recognition. International Conference on Robotics, Intelligent Systems and Signal Processing, Changsha, 2003: 880-885.[7]Chen S Y and Yi J K. A fuzzy neural network based on rough sets and its applications to chemical production process. Info-tech and Info-net, 2001. Proceedings, Beijing Oct 29- Nov 1, 2001, 4: 405-410.[8]Wu Z C. Research on remote sensing image classification using neural network based on rough sets. Info-tech and Info-net, 2001. Proceedings, Beijing Oct 29-Nov 1, 2001, 4: 79-284.[9]Liu H J, Tuo H Y, and Liu Y C. Rough neural network of variable precision[J].Neural Processing Letters.2004, 19(1):73-87[10]Dougherty J, Kohavi R, and Shami M. Supervised and unsupervised discretization of continuous features. Proceedings of 12th International Conference on Machine Learning, CA: Morgan Kaufmann, 1995: 194-202.Nguyen H S and Skowron A. Quantization of real-valued attributes, rough set and Boolean reasoning approaches. 2nd International Joint Conference on Information Sciences, Wrightsville Beach, NC, USA, 1995: 34-37.

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