NDLI: The analysis of a stochastic differential approach for Langevin competitive learning algorithm

Content Provider	IEEE Xplore Digital Library
Author	Jinwuk Seok Jeun-Woo Lee
Copyright Year	2002
Description	Author affiliation: Dept. of Internet Inf. Appliance, Korea Electron. & Telecommun. Res. Instn., Daejon, South Korea (Jinwuk Seok; Jeun-Woo Lee)
Abstract	Recently, various types of neural network models have been used successfully to applications in pattern recognition, control, signal processing, and so on. However, the previous models are not suitable for hardware implementation due to their complexity. In this paper, we present a survey of the stochastic analysis for the Langevin competitive learning algorithm, known for its easy hardware implementation. Since the Langevin competitive learning algorithm uses a time-invariant learning rate and a stochastic reinforcement term, it is necessary to analyze with stochastic differential or difference equation. The result of the analysis verifies that the Langevin competitive learning process is equal to the standard Ornstein-Uhlenback process and has a weak convergence property. The experimental results for Gaussian distributed data confirm the analysis provided in this paper.
Starting Page	80
Ending Page	83
File Size	232239
Page Count	4
File Format	PDF
ISBN	076951695X
ISSN	10514651
DOI	10.1109/ICPR.2002.1048242
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-08-11
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Stochastic processes Signal processing algorithms Hardware Neural networks Pattern recognition Process control Difference equations Convergence Data analysis
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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