NDLI: A hybrid neural network with fuzzy rules for categorical and numeric input

Content Provider	IEEE Xplore Digital Library
Author	Brouwer, R.K.
Copyright Year	2004
Description	Author affiliation: Dept. of Comput. Sci., Univ. Coll. of the Cariboo, Kamloops, BC, Canada (Brouwer, R.K.)
Abstract	This paper is concerned with the architecture and training of a hybrid neural network that may be used to represent a function that has both numeric and categorical independent variables. The numerical variables are separated from the categorical variables. It is assumed that the function to be represented really consists of several functions whose independent variables are the numerical variables. The hybrid network consists of two networks, the FFNN and the CIN (categorical input network). The FFNN accepts the numerical component as input and the CIN accepts the categorical input. Each produces a vector. These vectors are subsequently combined through the dot product to produce the final output of the combined network. The FFNN actually consists of several FFNN's with an MLP for each function to be represented. In general the output vector of the CIN will be 1-of-n and in that case the categorical component of the independent variables in effect is used to select the output of one of the MLP's and thus selects one of the functions represented. The approach suggested is shown to be quite effective.
Sponsorship	IEEE IEEE Syst., Man, and Cybernetics Soc. Univ. of Alberta, Faculty of Eng., Dept. of Elec. & Comput. Eng. Alberta Innovation & Sci. ASERC iCORE Alberta Ingenuity (AI) Fund Nat. Res. Council (NRC) Pearson Educ. Canada J. Wiley McGraw Hill Elsevier BV Inst. of Phys. Res. Studies Press SUN Microsystems Springer-Verlag Login Brothers Canada
Starting Page	319
Ending Page	324
File Size	306480
Page Count	6
File Format	PDF
ISBN	0780383761
DOI	10.1109/NAFIPS.2004.1336300
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-06-27
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Fuzzy neural networks Training data Computer science Computer architecture Input variables Labeling
Content Type	Text
Resource Type	Article

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