Abstract
When the traditional rough neural network is structured, The selection of initial weights are random values between (0,1).This article address this issue, proposed an application of rough set theory attribute importance, replaced with the attribute importance method of initial weights. Finally, with instance validation, compared to the traditional rough neural network,This method is not only to accelerate the network convergence rate, but also enhances the adaptability of BP neural network.
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Liu, B., Hao, S. (2011). Optimal Design of Weigh for Networks Based on Rough Sets. In: Liu, B., Chai, C. (eds) Information Computing and Applications. ICICA 2011. Lecture Notes in Computer Science, vol 7030. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25255-6_66
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DOI: https://doi.org/10.1007/978-3-642-25255-6_66
Publisher Name: Springer, Berlin, Heidelberg
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