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RETRACTED ARTICLE: Large-Scale Data Recommended Regulate Algorithm Based on Distributed Intelligent System Model under Cloud Environment

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This article was retracted on 09 November 2022

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Abstract

In the current work, appropriated regulate and manmade brainpower are joined in regulate engineering for Large Scale Systems (LSS). The point of this design is to give the overall arrangement and philosophy to achieve the ideal regulate in arranged appropriated situations where various conditions between sub-frameworks are found. Frequently these conditions or associations speak to regulate variables so the circulated regulate must be reliable for both subsystems and the ideal estimation of these variables needs to fulfil a shared objective. The point of the exploration portrayed in this paper is to abuse the alluring components of MPC in a disseminated usage consolidating learning strategies to play out the strategy in these variables in a helpful Multi Agent environment and concluded a Multi-Agent framework (MAS-MPC) to give pace, versatility, and with the computational exertion lessening. This methodology depends on strategic, participation and erudition. Aftereffects of the use of this design to a little portable system demonstrate that the subsequent directions of the recurrence of sign which is a regulate variable that can be adequate contrasted with the brought together arrangement. The application to a genuine system has been considered.

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Acknowledgements

This paper is financially supported by the following projects.

Supported by Science and Technology Planning Project of Jilin Province (20140520076JH).

Supported by Educational Commission of Jilin Province (2014B052).

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Authors and Affiliations

  1. Northeast Normal University, Changchun, People’s Republic of China

    Chuan Wan & Libiao Zhang

Authors
  1. Chuan Wan
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  2. Libiao Zhang
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Correspondence to Libiao Zhang.

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Wan, C., Zhang, L. RETRACTED ARTICLE: Large-Scale Data Recommended Regulate Algorithm Based on Distributed Intelligent System Model under Cloud Environment. Mobile Netw Appl 22, 674–682 (2017). https://doi.org/10.1007/s11036-017-0845-6

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  • DOI: https://doi.org/10.1007/s11036-017-0845-6

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