Modified ART1 neural networks for cell formation using production data

Ponnambalam, S. G.; Pandian, R.; Mahapatra, Siba Sankar; Saravanasankar, S.

doi:10.1109/coase.2008.4626507

Cited by 6 publications

(3 citation statements)

References 21 publications

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“…Moreover several applications related to R & D in power reactor simulation and fuel reactor structural materials performance can also be conducted. Research reactors may also be utilized in applications related to human health and electronic industry [5].…”

Section: Egypt's Second Research Reactor (Mpr) Openedmentioning

confidence: 99%

Real Time Accidents Diagnosis for Research Reactors using Adaptive Resonance Network

Kandil¹

2014

IJCNT

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Real time locating faults in any nuclear research reactors plants are the highest importance requirements, aimed for safety of human and environmental reasons. Since a great fault can occur in a few milliseconds, accordingly, there is an increasing demand for automated systems to diagnose such failures. Adaptive Resonance network (ART) [1] is a neurofuzzy network, which is an important family of competitive neural learning model. Its memory mode is very similar to that of biological one, and memory capacitycan increase while the learning patterns increase. It can perform real-time online learning, and can work under non stationary world. In this research a new proposed a neural network classifier based on ART, which achieved preferable results than several other neural algorithms will be presented. The proposed algorithm obtains and diagnosis faults accidents patterns in the Multi-Purpose Research Reactor of Egypt, to avoid the risk of occurrence of a nuclear accident.

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Section: Egypt's Second Research Reactor (Mpr) Openedmentioning

confidence: 99%

Real Time Accidents Diagnosis for Research Reactors using Adaptive Resonance Network

Kandil¹

2014

IJCNT

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“…It tried to overcome the limitations of the previously addressed ART1 models. Ponnambalam et al (2008) proposed another modified ART1 neural network model for cell formation using production data. An attempt was made to form disjoint machine cells using modified ART1 (adaptive resonance theory) to handle the real valued workload matrix.…”

Section: Ann In Gt/cm: From 2001 To 2012mentioning

confidence: 99%

“…,Won and Currie (2007),Ozdemir et al (2007), Yang and Yang (2008),Ponnambalam et al (2008),Pandian and Mahapatra (2009) andSengupta et al …”

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confidence: 99%

AI-based techniques in cellular manufacturing systems: a chronological survey and analysis

Ghosh

Sengupta

Doloi

et al. 2014

IJISE

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This article portrays a chronological review of the influence of artificial neural network in group technology applications in the vicinity of cellular manufacturing systems. The research trend is identified and the evolvement is captured through a critical analysis of the literature accessible from the very beginning of its practice in the early 90s till the 2012. Analysis of the diverse ANN approaches, spotted research pattern, comparison of the clustering efficiencies, the solutions obtained and the tools used make this study exclusive in its class.

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A discrete particle swarm optimisation approach for manufacturing cell formation

Mahapatra

Narkhede²,

Tambuskar³

2014

IJPM

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Modified ART1 neural networks for cell formation using production data

Cited by 6 publications

References 21 publications

Real Time Accidents Diagnosis for Research Reactors using Adaptive Resonance Network

Real Time Accidents Diagnosis for Research Reactors using Adaptive Resonance Network

AI-based techniques in cellular manufacturing systems: a chronological survey and analysis

A discrete particle swarm optimisation approach for manufacturing cell formation

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