NDLI: Review and performance analysis of single hidden layer sequential learning algorithms of feed-forward neural networks

Content Provider	IEEE Xplore Digital Library
Author	Li Bin Rong Xuewen
Copyright Year	2013
Description	Author affiliation: Sch. of Sci., Shandong Polytech. Univ., Jinan, China (Li Bin) \|\| Sch. of Control Sci. & Eng., Shandong Univ., Jinan, China (Rong Xuewen)
Abstract	The single hidden layer feed-forward neural networks learning algorithms with good performance are divided into batch learning and sequential (on-line) learning algorithms. Compared with batch learning algorithms, the sequential learning algorithms of feed-forward neural networks are suitable for problems of real-time processing, and are more adaptable for general industrial applications. This paper summarizes different sequential learning algorithms of single hidden layer feed-forward neural networks and analyzes the advantages and disadvantages of various algorithms. The performance including the stability, learning speed, approximation and generalization ability of different sequential learning algorithms are compared in detail in terms of different chaotic time series prediction problems. The simulation results provide the theoretic guidance on real applications of sequential learning algorithms of feed-forward neural networks.
Sponsorship	IEEE Control Syst. Soc.
Starting Page	2170
Ending Page	2175
File Size	328936
Page Count	6
File Format	PDF
ISBN	9781467355339
e-ISBN	9781467355346
DOI	10.1109/CCDC.2013.6561295
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Single hidden layer feed-forward neural networks Furnaces Neural networks Time series analysis Chaotic time series prediction Approximation algorithms Prediction algorithms Sequential learning algorithms Performance analysis Radio access networks
Content Type	Text
Resource Type	Article

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