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Production State Trend Prediction and Control for Industry Data by LS-Ann


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Abstract

The modern industry data is characterized by large volume, large variety, low density value and high processing velocity. Hence, it is difficult to use industry big data for effectively analyzing the trend and the production state by traditional methods. Aiming to solve a problem, a technology platform and data processing framework are established, and the LS-ANN (least square-artificial neural network) method is applied to process the industry big data by analyzing the corresponding technological process and the working principle. By efficiently processing the time series data, this method gives the industry production process with the ability of self-adaptation and fault tolerance. The effectiveness of the proposed method is demonstrated by experimental simulations.


Keywords


Pages

Total Pages: 8
Pages: 629-636

DOI
10.1080/10798587.2017.1316074


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Published

Volume: 23
Issue: 4
Year: 2017

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References

Conradie, A.V.E., and C. Aldrich. "Neurocontrol of a Ball Mill Grinding Circuit Using Evolutionary Reinforcement Learning." Minerals Engineering 14.10 (2001): 1277-1294. Crossref. Web. https://doi.org/10.1016/S0892-6875(01)00144-3

Ding, Wei et al. "Detection of Smell Change of Flue-Cured Tobacco Based on an Electronic Nose." Intelligent Automation & Soft Computing 19.2 (2013): 195-206. Crossref. Web. https://doi.org/10.1080/10798587.2013.787187

Hong, Tzung-Pei, Li-Huei Tseng, and Been-Chian Chien. "Mining from Incomplete Quantitative Data by Fuzzy Rough Sets." Expert Systems with Applications 37.3 (2010): 2644-2653. Crossref. Web. https://doi.org/10.1016/j.eswa.2009.08.002

Hong, Tzung-Pei, Li-Huei Tseng, and Been-Chian Chien. "Mining from Incomplete Quantitative Data by Fuzzy Rough Sets." Expert Systems with Applications 37.3 (2010): 2644-2653. Crossref. Web. https://doi.org/10.1016/j.eswa.2009.08.002

Hu H. IEEE

Kazakov, Andrei et al. "Predictive Correlations Based on Large Experimental Datasets: Critical Constants for Pure Compounds." Fluid Phase Equilibria 298.1 (2010): 131-142. Crossref. Web. https://doi.org/10.1016/j.fluid.2010.07.014

Koutsourakis, Nektarios, John G. Bartzis, and Nicolas C. Markatos. "Evaluation of Reynolds Stress, k-ε and RNG k-ε Turbulence Models in Street Canyon Flows Using Various Experimental Datasets." Environmental Fluid Mechanics 12.4 (2012): 379-403. Crossref. Web. https://doi.org/10.1007/s10652-012-9240-9

Lanouette, Robert, Jules Thibault, and Jacques L. Valade. "Process Modeling with Neural Networks Using Small Experimental Datasets." Computers & Chemical Engineering 23.9 (1999): 1167-1176. Crossref. Web. https://doi.org/10.1016/S0098-1354(99)00282-3

Leung C.K.S. IEEE International Congress

Li, Der-Chiang et al. "Using Mega-Trend-Diffusion and Artificial Samples in Small Data Set Learning for Early Flexible Manufacturing System Scheduling Knowledge." Computers & Operations Research 34.4 (2007): 966-982. Crossref. Web. https://doi.org/10.1016/j.cor.2005.05.019

Li, Der-Chiang, Fengming M. Chang, and Kuei-Ching Chen. "Building Reliability Growth Model Using Sequential Experiments and the Bayesian Theorem for Small Datasets." Expert Systems with Applications 37.4 (2010): 3434-3443. Crossref. Web. https://doi.org/10.1016/j.eswa.2009.10.001

Li, Der-Chiang, Liang-Sian Lin, and Li-Jhong Peng. "Improving Learning Accuracy by Using Synthetic Samples for Small Datasets with Non-Linear Attribute Dependency." Decision Support Systems 59 (2014): 286-295. Crossref. Web. https://doi.org/10.1016/j.dss.2013.12.007

Oniśko, Agnieszka, Marek J. Druzdzel, and Hanna Wasyluk. "Learning Bayesian Network Parameters from Small Data Sets: Application of Noisy-OR Gates." International Journal of Approximate Reasoning 27.2 (2001): 165-182. Crossref. Web. https://doi.org/10.1016/S0888-613X(01)00039-1

Rajamani, Raj K., and John A. Herbst. "Optimal Control of a Ball Mill Grinding circuit—I. Grinding Circuit Modeling and Dynamic Simulation." Chemical Engineering Science 46.3 (1991): 861-870. Crossref. Web. https://doi.org/10.1016/0009-2509(91)80193-3

Sofianos, Nikolaos A., and Yiannis S. Boutalis. "Multiple Models Fuzzy Control: A Redistributed Fixed Models Based Approach." Intelligent Automation & Soft Computing 20.2 (2014): 229-243. Crossref. Web. https://doi.org/10.1080/10798587.2013.863537

Wunderlich, Harald, and Martin B. Plenio. "Quantitative Verification of Entanglement and Fidelities from Incomplete Measurement Data." Journal of Modern Optics 56.18-19 (2009): 2100-2105. Crossref. Web. https://doi.org/10.1080/09500340903184303

JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present




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