Self-creating and organizing neural networks
Self-creating and organizing neural networks
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DOI：
10.1109/72.298226
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发表时间：
1994-07
影响因子：
--
通讯作者：
Doo-Il Choi;Sang-Hui Park
中科院分区：
文献类型：
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作者：
Doo-Il Choi;Sang-Hui Park
我们为人工神经网络开发了一种自我创造和组织的无监督学习算法。在本研究中，我们介绍了SCONN和SCONN2作为两个版本的自创建和组织神经网络（SCONN）算法。SCONN创建了一个自适应均匀矢量量化器（VQ），而SCONN2通过类似神经的架构创建了一个自适应非均匀矢量量化器。SCONN开始时只有一个输出节点，该节点具有足够宽的激活级别，并且激活级别会随着时间或激活历史而降低。SCONN自动决定是否调整现有节点的权重或创建新的“子节点”。它们与两种著名的算法——Kohonen的自组织特征映射（SOFM）（1988）作为神经VQ和Linde-Buzo-Gray （LBG）算法（1980）作为传统VQ进行了比较。结果表明，与其他算法相比，SCONN算法具有显著的优势。
We have developed a self-creating and organizing unsupervised learning algorithm for artificial neural networks. In this study, we introduce SCONN and SCONN2 as two versions of self-creating and organizing neural network (SCONN) algorithms. SCONN creates an adaptive uniform vector quantizer (VQ), whereas SCONN2 creates an adaptive nonuniform VQ by neural-like architecture. SCONN's begin with only one output node, which has a sufficiently wide activation level, and the activation level decrease depending upon the time or the activation history. SCONN's decide automatically whether to adapt the weights of existing nodes or to create a new "son node." They are compared with two famous algorithms-the Kohonen's self organizing feature map (SOFM) (1988) as a neural VQ and the Linde-Buzo-Gray (LBG) algorithm (1980) as a traditional VQ. The results show that SCONN's have significant benefits over other algorithms.