Autosoft Journal

Online Manuscript Access

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



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.



Total Pages: 8
Pages: 629-636


Manuscript ViewPdf Subscription required to access this document

Obtain access this manuscript in one of the following ways

Already subscribed?

Need information on obtaining a subscription? Personal and institutional subscriptions are available.

Already an author? Have access via email address?


Volume: 23
Issue: 4
Year: 2017

Cite this document


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.

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.

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.

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.


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.

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.

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.

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.

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.

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.

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.

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.

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.

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.


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
PREVIOUS DOI PREFIX (with T&F): 10.1080/10798587
InCites Journal IMPACT FACTOR (JIF) Data

2018  0.790
2017  0.652
2016  0.644

Scimago Journal and Country Rank (SJR) Data

2018  0.993
2017  0.655
2016  0.660
SJR: "The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric."

Journal: 1995-Present


TSI Press
18015 Bullis Hill
San Antonio, TX 78258 USA
PH: 210 479 1022
FAX: 210 479 1048