Fuzzy ART K-Means Clustering Technique: a hybrid neural network approach to cellularmanufacturing systems

Sengupta, Sourav; Ghosh, Tamal; Dan, Pranab K.

doi:10.1080/0951192x.2011.602362

Cited by 13 publications

(10 citation statements)

References 43 publications

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“…This method clearly outperforms the K-means method which is a standing cell formation technique and used heavily in past literatures. 3,13,58 MCDT model is also slightly quicker than the K-means method in terms of CPU time. A graphical analysis on the performance of the proposed approach and the improvement in terms of CPU time is presented in Figure 12.…”

Section: Resultsmentioning

confidence: 97%

“…These are processing time, processing sequence, machine utilization, production volume, available machining hours, machine breakdown time and many more. 13 Yet researchers are working with 0–1 CFP till present time, which is merely significant in practice at present. 14 The usage of ordinal data is being intensified in latest research of CMS.…”

Section: Introductionmentioning

confidence: 99%

“…Ratio data are considered in this research which are proposed by Venugopal and Narendran 3 and being used since then. 13,19,20 George et al 19 have stated that the workload data are usually considered as ratio data in CMS, and an altered machine-part matrix is obtained using this information. The overall processing time of the job could be obtained when the total production volume and the processing time per unit are multiplied.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Utilization-based grouping efficiency and multi-criteria decision approach in designing of manufacturing cells

Ghosh

Doloi

Dan

2016

Proceedings of the Institution of Mechanical Engineers, Part B:

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The essence of the “ratio data” in cellular manufacturing system has rarely been correctly emphasized since past few decades of research and study. This article is an attempt to deal with cell formation problem which exploits machine utilization percentage and eliminates the “fuzziness” on the subject of ratio data. In the course of this study, a distinctive technique is adopted based on multi-criteria decision technique with significant modifications which is experimented on test data and compared with published methodology. To verify the grouping result, a novel performance measure is proposed and elaborated analytically to establish its superiority and robustness over the earlier efficiency measures published in the past literature of cellular manufacturing system. The novelty of this research remains in eradicating ambiguities on the subject of “ratio data,” imposing appropriate rules while generating the test problems, catering a unique multi-criteria decision technique–based approach and a novel performance measure for the aforesaid problem.

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Section: Resultsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Utilization-based grouping efficiency and multi-criteria decision approach in designing of manufacturing cells

Ghosh

Doloi

Dan

2016

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…PUs take one or more input values, combine them into a single value using propagation rule, then transform them into an output value through an activation/transfer function. Complex networks can be constructed by connecting a number of PUs together (Sengupta et al, 2011). The simplest network is a group of PUs arranged in a single layer.…”

Section: Overview Of Annmentioning

confidence: 99%

“…The proposed SOM approach produced solutions with a grouping efficacy that is at least as good as any results earlier reported in the literature and improved the grouping efficacy for 70%. Sengupta et al (2011) demonstrated a new hybrid neural network approach, fuzzy ART K-means clustering technique (FAKMCT), to solve the PMG problem considering operation time. The performance of the proposed technique is compared to the existing clustering models such as simple K-means algorithm and modified ART1 algorithm as found in the recent literature.…”

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

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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Solubility prediction of gases in polymers using fuzzy neural network based on particle swarm optimization algorithm and clustering method

Huang

Liu

et al. 2013

J of Applied Polymer Sci

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A four-layer fuzzy neural network (FNN) model combining particle swarm optimization (PSO) algorithm and clustering method is proposed to predict the solubility of gases in polymers, hereafter called the CPSO-FNN, which combined fuzzy theory's better adaptive ability, neural network's capability of nonlinear and PSO algorithm's global search ability. In this article, the CPSO-FNN model has been employed to investigate solubility of CO 2 in polystyrene, N 2 in polystyrene, and CO 2 in polypropylene, respectively. Results obtained in this work indicate that the proposed CPSO-FNN is an effective method for the prediction of gases solubility in polymers. Meanwhile, compared with traditional FNN, this method shows a better performance on predicting gases solubility in polymers. The values of average relative deviation, squared correlation coefficient (R 2 ) and standard deviation are 0.135, 0.9936, and 0.0302, respectively. The statistical data demonstrate that the CPSO-FNN has an outstanding prediction accuracy and an excellent correlation between prediction values and experimental data.Recent years, the interdiscipline of information science and intelligent technology has a broad application perspective. 4,12,13 With the popularization of artificial neural networks (ANN), the determination of ANN structure, parameters and bias becomes the most crucial factors because the training process of ANN could be considered as a classical optimization problem. 12 Recently, researchers discovered that many intelligent algorithms such as genetic algorithm, 13 simulated annealing algorithm, 14 fuzzy logic theory, 15 gravitational search algorithm, 16 wavelet analysis, 17 ant colony optimization algorithm, 18 particle swarm optimization algorithm (PSO), 12,19-21 chaos theory, 22 and so on, can all be used for this determination. Therefore, ANN combined with intelligent optimization algorithms namely hybrid neural network has become one of the most active subject.So far as solubility of gases in polymers is concerned, it is affected by temperature, pressure, and sometimes it can also be affected by the interactions with the groups of the macromolecular chains. 23 As a result of the nonlinear relationship of these

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Fuzzy ART K-Means Clustering Technique: a hybrid neural network approach to cellularmanufacturing systems

Cited by 13 publications

References 43 publications

Utilization-based grouping efficiency and multi-criteria decision approach in designing of manufacturing cells

Utilization-based grouping efficiency and multi-criteria decision approach in designing of manufacturing cells

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

Solubility prediction of gases in polymers using fuzzy neural network based on particle swarm optimization algorithm and clustering method

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