NDLI: Fuzzy Relation-Based Neural Networks and Their Hybrid Identification

Content Provider	IEEE Xplore Digital Library
Author	Oh Sung-Kwun Pedrycz, W. Park Ho-Sung
Copyright Year	1963
Abstract	In this paper, we develop a comprehensive identification scheme for fuzzy relation-based neural networks (FRNNs). The proposed hybrid development approach combines the optimization technology of genetic algorithms (GAs) and an improved complex method introduced in the previous studies on fuzzy modeling. The structure of the FRNNs revolves around a collection of fuzzy rules and involves two types of fuzzy inference schemes. The taxonomy of these schemes relates to the format of the conclusion part of these rules (being either constants or linear functions). The optimization of the network deals with a number of essential parameters as well as the underlying learning mechanisms (e.g., apexes of membership functions, learning rates, and momentum coefficients). The hybrid identification approach helps achieve global optimization (when using GAs) and assure local convergence (that results from the use of the improved complex method). During the identification process, we are guided by a weighted objective function (performance index) in which a weighting factor is introduced to achieve a sound balance between approximation and generalization capabilities of the resulting model. The proposed identification method is applied to nonlinear processes (data) such as gas furnace process data and emission process data form a gas turbine power plant. The obtained experimental results show that the proposed networks exhibit high accuracy and generalization capabilities.
Sponsorship	IEEE Instrumentation and Measurement Society Bureau international des poids et mesures IEEE International Bureau of Weights and Measures (BIPM) URSI NBS Agency Ind. Sci. & Technol., Min. Int. Trade & Ind. Sci. Council Japan International Union of Pure and Applied Physics (IUPAP) NIST NRCC National Conference of Standards Laboratories (NCSL) Allen Osborne Associates Andeen-Hagerling, Inc. Ballantine Lab. Inc. Clarke-Hess Communications Research Corp. Fluke Corporation Guildline Instruments Hewlett-Packard Co. Julie Res. Lab. Inc. Keithley Instruments, Inc. Measurements International Ltd. Canada QuadTech Inc. Quantum Design Inc. Rotek Instrum. Corp. Tegam Inc. Tektronix Inc
Starting Page	2522
Ending Page	2537
Page Count	16
File Size	1482358
File Format	PDF
ISSN	00189456
Volume Number	56
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-12-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy neural networks Neural networks Optimization methods Genetic algorithms Taxonomy Learning systems Convergence Performance analysis Furnaces Turbines simplified and linear fuzzy inference Aggregate objective function (performance index) fuzzy relation-based neural networks (FRNNs) genetic algorithms (GAs) hybrid identification improved complex method
Content Type	Text
Resource Type	Article
Subject	Instrumentation Electrical and Electronic Engineering

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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