双向联想记忆集成 |
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作者姓名: | 王敏 储荣 |
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作者单位: | [1]河海大学计算机及信息工程学院,中国南京210098 [2]河海大学电气工程学院,中国南京210098 |
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摘 要: | 由多个尽可能多样化的分类器(前馈神经网络)组成的多分类器系统(MCS)能够显著地提高单个分类器的分类或推广能力.受MCS基本思想的启发,将集成引入到双向联想记忆快速学习(QLBAM)中,构建出一个BAM集成,旨在提高存储容量和纠错性能的同时,不破坏每个成员BAM的简单结构.计算机仿真表明,选择合适的"过剩生产与挑选并存"策略,即"稀疏算法"后,所提出的BAM集成在存储容量和抗噪声性能两个方面都显著优于单个QLBAM.
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关 键 词: | 双向联想记忆 神经网络集成 稀疏算法 bidirectional associative memory neural network ensemble thinning algorithm 双向联想记忆 集成 ENSEMBLE ASSOCIATIVE MEMORY algorithm thinning strategy choose Simulations show improving storage capacity capability simple structure component construct ensemble introduced quick |
修稿时间: | 2007/6/14 0:00:00 |
BIDIRECTIONAL ASSOCIATIVE MEMORY ENSEMBLE |
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Authors: | Wang Min Chu Rong |
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Abstract: | The multiple classifier system (MCS), composed of multiple diverse classifiers or feed-forward neural networks, can significantly improve the classification or generalization ability of a single classifier. Enlightened by the fundamental idea of MCS, the ensemble is introduced into the quick learning for bidirectional associative memory (QLBAM) to construct a BAM ensemble, for improving the storage capacity and the error-correction capability without destroying the simple structure of the component BAM. Simulations show that, with an appropriate "overproduce and choose" strategy or "thinning" algorithm, the proposed BAM ensemble significantly outperforms the single QLBAM in both storage capacity and noise-tolerance capability. |
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Keywords: | bidirectional associative memory neural network ensemble thinning algorithm |
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