A Soft Sensing Method Based on the Temporal Difference Learning Algorithm

Author

Ye, Tao ; Zhu, Xuefeng ; Li, Xiangyang

Author_Institution

Coll. of Autom. Sci. & Eng., South China Univ. of Technol., Guangzhou

Volume

1

fYear

0

fDate

0-0 0

Firstpage

4861

Lastpage

4865

Abstract

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

Keywords

learning (artificial intelligence); neurocontrollers; nonlinear control systems; paper pulp; Elman neural network; Kappa number prediction; batch kraft pulping process; dynamical process; local feedback; multilayer network; multistep prediction; soft sensing; supervised learning; temporal difference learning; temporal sequence; Artificial neural networks; Automation; Chemical processes; Chemical sensors; Educational institutions; Mathematical model; Multi-layer neural network; Neural networks; Process control; Supervised learning; Elman Neural Network; Kappa Number; Prediction; Soft Sensing; Temporal Difference;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1713308

Filename

1713308