Convergence analysis of batch gradient algorithm for three classes of sigma-pi neural networks
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论文类型：期刊论文
发表时间：2007-12-01
发表刊物：NEURAL PROCESSING LETTERS
收录刊物：SCIE、EI、Scopus
卷号：26
期号：3
页面范围：177-189
ISSN号：1370-4621
关键字：convergence; sigma-Pi-Sigma neural networks; sigma-Sigma-Pi neural networks; sigma-Pi-Sigma-Pi neural networks; batch gradient algorithm; monotonicity
摘要：Sigma-Pi (Sigma u-Sigma a) neural networks (SPNNs) are known to provide more powerful mapping capability than traditional feed-forward neural networks. A unified convergence analysis for the batch gradient algorithm for SPNN learning is presented, covering three classes of SPNNs: Sigma u-Sigma a-Sigma u, Sigma u-Sigma u-Sigma a and Sigma u-Sigma a-Sigma u-Sigma a. The monotonicity of the error function in the iteration is also guaranteed.
发表时间：2007-12-01