Convergence Analysis of Batch Gradient Algorithm for Three Classes of Sigma-Pi Neural Networks

Sigma-Pi (Σ-Π) 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: Σ-Π-Σ, Σ-Σ-Π and Σ-Π-Σ-Π. The m...

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Bibliographic Details
Published in:Neural processing letters Vol. 26; no. 3; pp. 177 - 189
Main Authors: Zhang, Chao, Wu, Wei, Xiong, Yan
Format: Journal Article
Language:English
Published: Dordrecht Springer 01.12.2007
Springer Nature B.V
Subjects:
Algorithms
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Connectionism. Neural networks
Convergence
Error functions
Exact sciences and technology
Machine learning
Neural networks
Sigma-Pi-Sigma-Pi neural networks, Batch gradient algorithm
Gradient
Error function
74P05
Monotonicity, 92B20
Neural network
Sigma-Sigma-Pi neural networks
Search algorithm
Batch process
Batch production
Convergence, Sigma-Pi-Sigma neural networks
Learning algorithm
Artificial intelligence
68Q32
Algorithm analysis
Monotonicity
ISSN:1370-4621, 1573-773X
Online Access:Get full text
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Summary:Sigma-Pi (Σ-Π) 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: Σ-Π-Σ, Σ-Σ-Π and Σ-Π-Σ-Π. The monotonicity of the error function in the iteration is also guaranteed.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1370-4621
1573-773X
DOI:10.1007/s11063-007-9050-0