Abstract In this paper, an integrated neurofuzzy-JITL model is proposed for batch processes. The neurofuzzy is employed to build a global model with its excellent extrapolating ability while Just-in-Time Learning (JITL) is used to build the local ARX model due to its good local dynamic modeling ability. In addition, Simulated Annealing (SA) algorithm is adopted to obtain the optimal weights of two models. As a result, the integrated model has better global generalization ability and higher accuracy. Lastly, the effectiveness of the presented integrated neurofuzzy-JITL model is verified by example.


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    Title :

    Integrated Neurofuzzy-JITL Model and Its Application in Batch Processes


    Contributors:
    Fu, Zhao (author) / Jia, Li (author)


    Publication date :

    2014-01-01


    Size :

    11 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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