SI Engine Emissions Model Based on Dynamic Neural Networks and D-Optimality

2005 ◽  
Author(s):  
Mohamed Ayeb ◽  
Heinz J. Theuerkauf ◽  
Thomas Winsel
Keyword(s):  
Neural Networks ◽  
Si Engine ◽  
Engine Emissions ◽  
Dynamic Neural Networks ◽  
Model Based

Model Based Control of Diesel Engines with Dynamic Neural Networks

2002 ◽  
Vol 35 (2) ◽  
pp. 325-330
Author(s):  
Rolf Isermann ◽  
Michael Hafner ◽  
Matthias Weber
Keyword(s):  
Neural Networks ◽  
Diesel Engines ◽  
Dynamic Neural Networks ◽  
Model Based Control ◽  
Model Based

A global 3-D electron density reconstruction model based on radio occultation data and neural networks

2021 ◽  
pp. 105702
Author(s):  
John Bosco Habarulema ◽  
Daniel Okoh ◽  
Dalia Burešová ◽  
Babatunde Rabiu ◽  
Mpho Tshisaphungo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Electron Density ◽  
Radio Occultation ◽  
Model Based ◽  
Occultation Data ◽  
Reconstruction Model ◽  
Radio Occultation Data

scholarly journals A New Robust Training Law for Dynamic Neural Networks with External Disturbance: An LMI Approach

2010 ◽  
Vol 2010 ◽  
pp. 1-14 ◽  
Author(s):  
Choon Ki Ahn
Keyword(s):  
Neural Networks ◽  
Linear Matrix Inequality ◽  
Exponential Stability ◽  
External Disturbance ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Input Output ◽  
Lmi Approach ◽  
Dynamic Neural Networks ◽  
Numerical Examples

A new robust training law, which is called an input/output-to-state stable training law (IOSSTL), is proposed for dynamic neural networks with external disturbance. Based on linear matrix inequality (LMI) formulation, the IOSSTL is presented to not only guarantee exponential stability but also reduce the effect of an external disturbance. It is shown that the IOSSTL can be obtained by solving the LMI, which can be easily facilitated by using some standard numerical packages. Numerical examples are presented to demonstrate the validity of the proposed IOSSTL.

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