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Inventi Impact - Engineering Mathematics

Articles

  • Inventi:eem/64/14
    D-FNN BASED MODELING AND BP NEURAL NETWORK DECOUPLING CONTROL OF PVC STRIPPING PROCESS
    Shu-zhi Gao, Jing Yang, Jie-sheng Wang

    PVC stripping process is a kind of complicated industrial process with characteristics of highly nonlinear and time varying. Aiming at the problem of establishing the accurate mathematics model due to the multivariable coupling and big time delay, the dynamic fuzzy neural network (D-FNN) is adopted to establish the PVC stripping process model based on the actual process operation datum. Then, the PVC stripping process is decoupled by the distributed neural network decoupling module to obtain two singleinput- single-output (SISO) subsystems (slurry flow to top tower temperature and steamflow to bottomtower temperature). Finally, the PID controller based on BP neural networks is used to control the decoupled PVC stripper system. Simulation results show the effectiveness of the proposed integrated intelligent control method.

    How to Cite this Article
    CC Compliant Citation: Shu-zhi Gao, Jing Yang, and Jie-sheng Wang, “D-FNN Based Modeling and BP Neural Network Decoupling Control of PVC Stripping Process,” Mathematical Problems in Engineering, vol. 2014, Article ID 681259, 13 pages, 2014. doi:10.1155/2014/681259. Copyright © 2014 Shu-zhi Gao et al. This article is distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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