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- W2019683353 abstract "A time-varying neuro-fuzzy model (TNFM) based on the probability density function (PDF) technique is proposed in this paper. It is able to describe the characteristics of repetition of batch process by using the input–output data information of batch axes and time axes separately. More specifically, the parameters of time-varying neuro-fuzzy are functions of time during one batch, while it can be looked as uniform values from the viewpoint of batch to batch. Moreover, it converts the modelling problem into a PDF shape of modelling error control problem. The adjustable parameters of TNFM are updated to make the PDF shape of modelling error to follow the targeted PDF. As a result, the proposed method not only describes the characteristics of repetition of batch process, but also guarantees the generalization and robustness, especially when uncertainty and disturbance exist. Lastly, to verify the effectiveness of the proposed identification method, it is applied to a benchmark batch process, in comparison with the traditional identification algorithm based on minimum variance and zero mean." @default.
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- W2019683353 date "2013-04-02" @default.
- W2019683353 modified "2023-09-23" @default.
- W2019683353 title "Time-varying neuro-fuzzy model using probability density function techniques for batch processes" @default.
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- W2019683353 doi "https://doi.org/10.1080/00949655.2013.782027" @default.
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