NDLI: Improved MS_CMAC Neural Networks by Integrating a Simplified UFN Model

Content Provider	Springer Nature Link
Author	Jan, Jiun Chi Hung, Shih Lin
Copyright Year	2007
Abstract	Macro_Structure_CMAC (MS_CMAC) is a variational CMAC neural network that is designed for modeling smooth functional mappings. The MS_CMAC learning strategy involves constructing virtual grid-distributed data points from random-distributed training data points, and then using the virtual data points to train a tree structure network that is composed of one-dimensional CMAC nodes. A disadvantage of the MS_CMAC is that the prediction errors near the boundary area might sometimes be unexpectedly large. Another disadvantage of the MS_CMAC is that generating virtual grid-distributed data points generally takes a long computational time. Therefore, this study develops an improved model by integrating an unsupervised fuzzy neural network (UFN) into the MS_CMAC to initialize systematically the virtual grid-distributed data points. Additionally, a new error feedback ratio function is adopted to speed up the MS_CMAC training. Several numerical problems are considered to test the improved MS_CMAC. The computed results indicate that a simplified UFN model can produce good initial values of the virtual grid-distributed data points to aggrandize MS_CMAC training. The MS_CMAC prediction is also improved by using the initialized virtual grid-distributed data points.
Starting Page	163
Ending Page	177
Page Count	15
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	27
Issue Number	2
e-ISSN	1573773X
Language	English
Publisher	Springer US
Publisher Date	2007-12-13
Publisher Place	Boston
Access Restriction	Subscribed
Subject Keyword	Virtual grid-distributed data points A tree structure network UFN Operations Research/Decision Theory Electronic and Computer Engineering Artificial Intelligence (incl. Robotics) Complexity
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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