| 摘要: |
| 文章提出了一种新的基于遗传策略和模糊ART(adaptive resonance theory)神经网络的非监督分类方法.首先,利用原有的训练样本对模糊ART神经网络进行非监督训练，然后,采用遗传策略为模糊ART神经网络增加各类族边界邻域内的训练样本点，再对模糊ART神经网络进行有监督训练.这种方法解决了训练样本在较少条件下的ART系列神经网络的学习与分类问题，提高了ART系列神经网络的分类性能，并扩展了其应用范围. |
| 关键词: 神经网络，遗传算法，ART(adaptive resonance theory)，非监督分类. |
| DOI： |
| 分类号: |
| 基金项目:本文研究得到江西省自然科学基金资助. |
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| The Unsupervised Classification Using Evolutionary Strategies and Neural Networks |
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LI Ming,YAN Chao-hua,LIU Gao-hang
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| Abstract: |
| A new unsupervised classification method using evolutionary strategies and fuzzy ART (adaptive resonance theory) neural networks is proposed in this paper. First, fuzzy ART neural networks is trained by original input samples under unsupervised way. Then evolutionary strategies is used to generate new training samples near the clusters boundaries of neural networks. Therefore the weights of fuzzy ART neural networks can be revised and refined by training those new generated samples under supervised way. The proposed method resolves the training problem for ART serial neural networks when there are only less training samples available. Consequently, it enhances the performance of ART serial neural networks and extends their application. |
| Key words: Neural network, genetic algorithms, ART(adaptive resonance theory), unsupervised classification. |
