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An architecture-adaptive neural network online control system

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Abstract

An architecture-adaptive intelligent self-tuning control system is presented. The system is composed of the supervisor module, the model refinement module, the process plant and the database. In the supervisor module, the user prescribes the desired curve for the plant dynamic process. The model refinement module is in parallel with the process plant, and consists of the self-tuning process model, which contains an architecture-adaptive neural network. The model refinement module could learn intelligently the real process plant by the prompt adjustments based on the difference of the outputs of the two modules, and its learned model is also refined gradually. This diagram is especially versatile in the complex nonlinear and time-variant systems in practice.

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Acknowledgments

The author would thank the anonymous reviewers for their valuable comments and suggestions which helped improve the paper greatly.

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Authors and Affiliations

  1. Institute of Computer Science and Technology, Peking University, Beijing, 100871, China

    Xun Liang

  2. Department of Economics and Operations Research, Stanford University, Stanford, CA, 95035, USA

    Xun Liang

  3. Department of Logistics Engineering and Management, National Taichung Institute of Technology, Taichung, 404, Taiwan

    Rong-Chang Chen

  4. Institute of Computer Science and Technology, Peking University, Beijing, 100871, China

    Jian Yang

Authors
  1. Xun Liang
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  2. Rong-Chang Chen
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  3. Jian Yang
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Correspondence to Rong-Chang Chen.

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Liang, X., Chen, RC. & Yang, J. An architecture-adaptive neural network online control system. Neural Comput & Applic 17, 413–423 (2008). https://doi.org/10.1007/s00521-007-0137-3

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  • DOI: https://doi.org/10.1007/s00521-007-0137-3

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