Nonlinear moving average dynamic soft sensor model with FIR filter
The nonlinear moving average(NMA)model has been widely applied in the field of dynamic soft sensing.However,due to the high model complexity,the averaging horizon of the NMA model is relatively short.As a result,its accuracy could be compromised if the industrial process embodies long time delay or strong measurement noise.To solve this problem,a novel soft sensor modeling strategy,with relatively strong anti-interference ability and long averaging horizon,is proposed by combining the NMA model with the FIR filter.Besides,parameters in the NMA model and FIR filter are optimized synchronously based on the Adam algorithm.Meanwhile,the layer whitening strategy is designed to avoid the parameter coupling phenomenon between the two structures mentioned.Finally,numerical simulations and sulfur recovery process modeling experiments have been conducted to verify the prediction accuracy of the proposed model and the rationality of model design.
万方数据
维普
