Convergence of BP algorithm for product unit neural networks with exponential weights
Release Time:2019-03-10 Hits:
Indexed by: Journal Article
Date of Publication: 2008-12-01
Journal: NEUROCOMPUTING
Included Journals: Scopus、EI、SCIE
Volume: 72
Issue: 1-3
Page Number: 513-520
ISSN: 0925-2312
Key Words: Neural network; Product unit; Exponential weights; Back-propagation algorithm; Convergence
Abstract: Product unit neural networks with exponential weights (PUNNs) can provide more powerful internal representation capability than traditional feed-forward neural networks. In this paper, a convergence result of the back-propagation (BP) algorithm for training PUNNs is presented. The monotonicity of the error function in the training iteration process is also guaranteed. A numerical example is given to support the theoretical findings. (c) 2008 Elsevier B.V. All rights reserved.