Cascade process modeling with mechanism-based hierarchical neural networks.
Cascade process, such as wastewater treatment plant, includes many nonlinear sub-systems and many variables. When the number of sub-systems is big, the input-output relation in the first block and the last block cannot represent the whole process. In this paper we use two techniques to overcome the above problem. Firstly we propose a new neural model: hierarchical neural networks to identify the cascade process; then we use serial structural mechanism model based on the physical equations to connect with neural model. A stable learning algorithm and theoretical analysis are given. Finally, this method is used to model a wastewater treatment plant. Real operational data of wastewater treatment plant is applied to illustrate the modeling approach.
Cong
Qiumei
Q
Key Laboratory of Process Industry Automation, Northeastern University, Shenyang, 110006, China.
Yu
Wen
W
Chai
Tianyou
T
D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted