An uncertain control framework of cloud model

  • Authors:

  • Baohua Cao;Deyi Li;Kun Qin;Guisheng Chen;Yuchao Liu;Peng Han

  • Affiliations:

  • Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA;The Institute of Beijing Electronic System Engineering, Beijing, China;School of Remote Sensing Information Engineering, Wuhan University, China;The Institute of Beijing Electronic System Engineering, Beijing, China;Department of Computer Science and Technology, Tsinghua University, China;Chongqing Academy of Science and Technology, ChongQing, China

  • Venue:
  • RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
  • Year:
  • 2010

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Abstract

The mathematical representation of a concept with uncertainty is one of the foundations of Artificial Intelligence. Uncertain Control has been the core in VSC systems and nonlinear control systems, as the representation of Uncertainty is required. Cloud Model represents the uncertainty with expectation Ex, entropy En and Hyper-entropy He by combining Fuzziness and Randomness together. Randomness and fuzziness make uncertain control be a difficult problem, hence we propose an uncertain control framework of Cloud Model called UCF-CM to solve it. UCF-CM tunes the parameters of Ex, En and He with Cloud, Cloud Controller and Cloud Adapter to generate self-adaptive control in dealing with uncertainties. Finally, an experience of a representative application with UCF-CM is implemented by controlling the growing process of artificial plants to verify the validity and feasibility.