NDLI: Model selection for RBF neural networks using distorter

Content Provider	IEEE Xplore Digital Library
Author	Miyoshi, T. Ichihashi, H. Tabuchi, H. Tanaka, H.
Copyright Year	1999
Description	Author affiliation: Dept. of Ind. Eng., Osaka Prefecture Univ., Japan (Miyoshi, T.)
Abstract	We propose an unbiasedness criterion using distorter (UCD) which is a heuristic model selection criterion, and apply it to determining the number of hidden units of RBF networks. The criterion is defined as the difference between outputs of two RBF networks with the same architecture, the one is trained to minimize the ordinary training error and the other is trained to minimize the error between the training data and output of the network transformed by the nonlinear function called "distorter". In order to compare the performance of proposed criterion with other criteria such as AIC and NIC, we carried out some numerical simulations of the model selection.
Starting Page	38
Ending Page	43
File Size	571211
Page Count	6
File Format	PDF
ISBN	0780354060
ISSN	10987584
DOI	10.1109/FUZZY.1999.793203
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-08-22
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Radial basis function networks User centered design Training data Nonlinear distortion Fuzzy neural networks Industrial engineering Numerical simulation Computer networks Tomography
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Theoretical Computer Science Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Fuzzy neural network with general parameter adaptation for modeling of nonlinear time-series

Interval Type-2 Fuzzy Membership Function Design and its Application to Radial Basis Function Neural Networks

An Adaptive Hybrid Architecture for Intrusion Detection Based on Fuzzy Clustering and RBF Neural Networks

Comments on Functional equivalence between radial basis function networks and fuzzy inference systems [with reply]

An Incremental Design of Radial Basis Function Networks

A simplified model of fuzzy inference system constructed by using RBF neurons

A fast fuzzy modelling approach using clustering neural networks

Training Reformulated Radial Basis Function Neural Networks Capable of Identifying Uncertainty in Data Classification

Higher-Order-Statistics-Based Radial Basis Function Networks for Signal Enhancement

Model selection for RBF neural networks using distorter

Similar Documents

Fuzzy neural network with general parameter adaptation for modeling of nonlinear time-series

Interval Type-2 Fuzzy Membership Function Design and its Application to Radial Basis Function Neural Networks

An Adaptive Hybrid Architecture for Intrusion Detection Based on Fuzzy Clustering and RBF Neural Networks

Comments on Functional equivalence between radial basis function networks and fuzzy inference systems [with reply]

An Incremental Design of Radial Basis Function Networks

A simplified model of fuzzy inference system constructed by using RBF neurons

A fast fuzzy modelling approach using clustering neural networks

Training Reformulated Radial Basis Function Neural Networks Capable of Identifying Uncertainty in Data Classification

Higher-Order-Statistics-Based Radial Basis Function Networks for Signal Enhancement

Model selection for RBF neural networks using distorter