Model Order Reduction of Nonlinear Circuit using Proper Orthogonal Decomposition and Nonlinear Autoregressive with eXogenous input (NARX) Neural Network

2018 ◽  
Author(s):  
S. Nagaraj ◽  
D. Seshachalam ◽  
Sunil Hucharaddi
Keyword(s):  
Neural Network ◽  
Proper Orthogonal Decomposition ◽  
Model Order Reduction ◽  
Order Reduction ◽  
Orthogonal Decomposition ◽  
Nonlinear Circuit ◽  
Model Order ◽  
Proper Orthogonal ◽  
Nonlinear Autoregressive ◽  
Narx Neural Network

scholarly journals SCR-Filter Model Order Reduction (2): Proper Orthogonal Decomposition and Artificial Neural Network

2020 ◽  
Vol 6 (4) ◽  
pp. 410-430
Author(s):  
Seun Olowojebutu ◽  
Thomas Steffen ◽  
Phillip Bush
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Model Order Reduction ◽  
Order Reduction ◽  
Orthogonal Decomposition ◽  
High Fidelity ◽  
Model Order ◽  
Filter Model ◽  
Proper Orthogonal ◽  
Simulation Time

Abstract Catalysed diesel particulate filters (DPF) have been described as multifunctional reactor systems. Integration of selective catalytic reduction (SCR) functionality in the DPF creates an SCR-in-DPF system that achieves nitrous oxides (NOx) treatment along with particulate matter (PM) collection. The physical and chemical aspects of the integrated SCR-filter complicate system modelling. The goal of this work is to develop low-complexity model of the SCR-filter system which retains high fidelity. A high-fidelity model of the SCR-coated filter has been developed and validated. The performance of the model was described in a previous paper. Model complexity reduction is attempted in this paper. The objective is to achieve simulation times that can support the deployment of the model for online system control in an engine control unit. Two approaches were taken for the SCR-coated filter model order reduction (MOR): a “grey-box” approach via proper orthogonal decomposition (POD) and a “black box” approach via artificial neural network (ANN) function approximation. The POD method is shown to deliver a significant MOR while maintaining a high degree of fidelity but with less than 5% improvement in simulation time. The ANN method delivers a substantial MOR with reduction of three orders of magnitude in simulation time. The accuracy of the ANN model is satisfactory with good generalisation to new test data but noticeably inferior to the POD method.

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