| 摘要: |
| 将ICBP网络模型引入BP-SOM(self-organizing feature maps)网络体系结构,并构建了一个基于SOM模型的集成型网络ICBP-SOM.BP-SOM是Ton Weijters提出的一种学习算法,它的目标是克服BP网络在特定类型的学习样本中存在的知识推广性方面的严重缺陷.提出此集成型网络的动机是,利用BP-SOM良好的知识解释能力和ICBP网络良好的推广性和自适应性构造一个ICBP-SOM模型,它具有良好的知识表示能力和极具竞争力的推广性能.在6个基准数据集上的实验结果验证了这一集成型网络的可行性和有效性. |
| 关键词: 神经网络 圆型反向传播网络 改进的圆型反向传播网络 自组织特征映射 BP-SOM 分类 |
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| 基金项目:Supported by the National Natural Science Foundation of China under Grant No.60773061 (国家自然科学基金); the Jiangsu Ph.D. Students Innovative Foundation of China under Grant No.BCXJ05-05 (江苏省博士生创新基金) |
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| Hybrid Neural Network Architecture Based on Self-Organizing Feature Maps |
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DAI Qun,CHEN Song-Can,WANG Zhe
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| Abstract: |
| An SOM(self-organizing feature maps)-based integrated network, namely ICBP-SOM, is constructed by applying the ICBP network model to the BP-SOM architecture. BP-SOM is a learning algorithm put forward by Ton Weijters, which aims to overcome some of the serious limitations of BP in generalizing knowledge from certain types of learning material. The motivation of presenting the integration is to employ BP-SOM good knowledge interpretation ability and the ICBP good generalization and adaptability to construct an ICBP-SOM, which processes favorable knowledge representation capability and competitive generalization performance. The experimental results on six benchmark data sets validate the feasibility and effectiveness of the integration. |
| Key words: neural network circular back-propagation neural network (CBP) improved circular back- propagation neural network (ICBP) self-organizing feature maps (SOM) BP-SOM classification |
