A Soft Sensing Method Based on the Temporal Difference Learning Algorithm

Soft sensing methods were widely studied due to their attractive properties. The soft sensing models based on supervised learning neural networks were well researched in the last decade. This paper proposes a soft sensing method based on the temporal difference (TD) learning. TD methods are more preferable to deal with multi-step prediction problems that involve temporal sequences of a dynamical process. The soft sensor is implemented with an Elman neural network, a multilayer network with local feedback, which is trained by the TD algorithm. Finally, the TD-based soft sensor is applied to the Kappa number prediction in the batch kraft pulping process