NDLI: Approximate Knowledge Extraction using MRA for TYPE-I Fuzzy Neural Networks

Content Provider	IEEE Xplore Digital Library
Author	Burney, S.M.A. Jilani, T.A. Saleemi, M.A.
Copyright Year	2006
Description	Author affiliation: Dept. of Comput. Sci., Karachi Univ. (Burney, S.M.A.; Jilani, T.A.; Saleemi, M.A.)
Abstract	Using neural network, we try to model the unknown function/for given input-output data pairs. The connection strength of each neuron is updated through learning. Repeated simulations of crisp neural network produce different values of weight factors that are directly affected by the change of different parameters. We propose the idea that for each neuron in the network, we can obtain reduced model with higher efficiency using wavelet based multi-resolution analysis (MRA) to form wavelet based quasi fuzzy weight sets (WBQFWS) through repeated simulation of the crisp neural network. Such type of WBQFWS provides good initial solution for training type-I fuzzified neural networks. As real data is subjected to noise and uncertainty, therefore, WBQWFS help in the simplification of the structure of the complex problems using low dimensional data sets. Such fuzzy sets are also supportive in approximating the sum of knowledge that a hidden or output neuron contains in the learning frameworks
Sponsorship	IEEE Islamabad Sect. Mohammed Ali Jinnah Univ. Pakistan Inst. of Eng. & Appl. Sci
Starting Page	1
Ending Page	6
File Size	4654841
Page Count	6
File Format	PDF
ISBN	1424404568
DOI	10.1109/ICEIS.2006.1703212
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-04-22
Publisher Place	Pakistan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	wavelet based Quasi fuzzy weight sets (WBQFWS) wavelets based multiresolution analysis (MRA) Neurons Humans Artificial neural networks Wavelet analysis crisp neural networks Biological neural networks Multiresolution analysis Fuzzy sets extraction of fuzzy rules fuzzy neural network Neural networks Fuzzy neural networks Signal analysis
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

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