NDLI: Study on Ventilation System Reliability Early-Warning Based on RS-ANN

Content Provider	IEEE Xplore Digital Library
Author	Hong-de Wang Yi Zhao
Copyright Year	2009
Abstract	System reliability early warning aims to measure the rate of tolerance deviation from the reliability index borderline of the system running states, to confirm the early warning grade, and to assist decision-making warning. Analyzed and compared the methods of system early warning from home and abroad, and combined Rough Set (RS) theory and Artificial Neuron Network (ANN) technique, a new method based on Rough Set and Artificial Neuron Network (RS-ANN) to solve ventilation system reliability early warning is put forward. Firstly, an index system which adapted to ANN analysis for mine ventilation system reliability early warning is established. Secondly, a prepositive system with RS method to optimize the index system of ANN is put forward. Thirdly, the simulation model for ventilation system reliability early warning which based on RS-ANN is set up. Finally, the effectiveness of this method is proved by an example. Computer simulation shows that the simulation result by RS-ANN methods is consistent with the result of ANN analysis, but the training efficiency increased 667 times.
Starting Page	598
Ending Page	602
File Size	477080
Page Count	5
File Format	PDF
ISBN	9780769537351
DOI	10.1109/FSKD.2009.809
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computational modeling Computer simulation Neurons Decision making Optimization methods Reliability theory Reliability engineering ventilation system Ventilation Safety reliability early-warning Rough Set and Artificial Neuron Network (RS-ANN) Fuzzy systems index system
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

Evaluation Coupling Model of Mine Ventilation System Based on RS and ANN

Notice of RetractionReliability evaluation based on RS-ANN

Multi-attribute decision-making based on rough set theory and modified PBMF-index function

Research of Groundwater Environment Early Warning Based on Intelligent Algorithm

An Improved Economic Early Warning Based on Rough Set and Support Vector Machine

Evaluating fuzzy reliability using rough intuitionistic fuzzy set

A survey of decision making methods based on certain hybrid soft set models

Bid/no-bid decision-making using rough sets and neural networks

OR/AND neuron in modeling fuzzy set connectives

Study on Ventilation System Reliability Early-Warning Based on RS-ANN

Similar Documents

Evaluation Coupling Model of Mine Ventilation System Based on RS and ANN

Notice of RetractionReliability evaluation based on RS-ANN

Multi-attribute decision-making based on rough set theory and modified PBMF-index function

Research of Groundwater Environment Early Warning Based on Intelligent Algorithm

An Improved Economic Early Warning Based on Rough Set and Support Vector Machine

Evaluating fuzzy reliability using rough intuitionistic fuzzy set

A survey of decision making methods based on certain hybrid soft set models

Bid/no-bid decision-making using rough sets and neural networks

OR/AND neuron in modeling fuzzy set connectives

Study on Ventilation System Reliability Early-Warning Based on RS-ANN