论文信息 - A multi-model soft sensing method based on D-S and ARIMA model

A multi-model soft sensing method based on D-S and ARIMA model

There are some disadvantages in the traditional model algorithm for the soft sensor,such as low predictive accuracy and poor fusion ability.Therefore,a multi-model soft sensor algorithm is proposed based on the D-S rule and difference autoregressive moving average(ARIMA) model.Firstly,the adaptive fuzzy kernel clustering method(AFKCM)and least squares support vector machine(LS-SVM) are used to establish multiple sub-models.Then the output of the soft sensor is obtained through the fusion of the sub-models based on the weight factor calculated by D-S rules.The ARIMA model is used to realize the dynamic correction to the static multi-model output.Simulation results and industry application indicate that,comparing with the traditional soft sensor,the proposed method has better predictive performance and fusion ability.

Wang Zhen-le

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