Modeling of Stripper Temperature based on Improved T-S Fuzzy Neural Network

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Provided by: Academy Publisher
Topic: Networking
Format: PDF
In the PolyVinyl Chloride (PVC) industry, proper control of stripper temperature is directly related to product quality of PVC resin. Considering multivariable, strong coupling, nonlinear and time-varying characteristics of the temperature control system for PVC stripper the current modeling method is difficult to obtain a relatively accurate mathematical model. Then, this paper studies the stripper temperature modeling method based on improved T-S fuzzy neural network, and proposes new nearest neighbor clustering fuzzy rules. In order to improve the learning performance, hybrid learning algorithm based on T-S fuzzy neural networks is developed.
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