Aerodynamic Parameter Prediction on a Airfoil with Flap via Artificial Hair Sensors and Feedforward Neural Network

2016 ◽  
Author(s):  
Kaman S. Thapa Magar ◽  
Gregory W. Reich ◽  
Matthew Rickey ◽  
Brian Smyers ◽  
Richard Beblo
Keyword(s):  
Neural Network ◽  
Feedforward Neural Network ◽  
Aerodynamic Parameter ◽  
Hair Sensors ◽  
Parameter Prediction

Aerodynamic parameters from distributed heterogeneous CNT hair sensors with a feedforward neural network

2016 ◽  
Vol 11 (6) ◽  
pp. 066006 ◽  
Author(s):  
Kaman Thapa Magar ◽  
Gregory W Reich ◽  
Corey Kondash ◽  
Keith Slinker ◽  
Alexander M Pankonien ◽  
...  
Keyword(s):  
Neural Network ◽  
Feedforward Neural Network ◽  
Aerodynamic Parameters ◽  
Hair Sensors

scholarly journals Aerodynamic Parameter Prediction via Artificial Hair Sensors with Signal Power in Turbulent Flow

AIAA Journal ◽  
2019 ◽  
Vol 57 (3) ◽  
pp. 898-903
Author(s):  
Kaman Thapa Magar ◽  
Alexander M. Pankonien ◽  
Gregory W. Reich ◽  
Richard V. Beblo
Keyword(s):  
Turbulent Flow ◽  
Signal Power ◽  
Aerodynamic Parameter ◽  
Hair Sensors ◽  
Parameter Prediction

Operational Determination of the Activated Sludge Process Using Neural Networks

1992 ◽  
Vol 26 (9-11) ◽  
pp. 2461-2464 ◽  
Author(s):  
R. D. Tyagi ◽  
Y. G. Du
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Steady State ◽  
Activated Sludge ◽  
Feedforward Neural Network ◽  
Training Data ◽  
Activated Sludge Process

A steady-statemathematical model of an activated sludgeprocess with a secondary settler was developed. With a limited number of training data samples obtained from the simulation at steady state, a feedforward neural network was established which exhibits an excellent capability for the operational prediction and determination.

scholarly journals On one approach to the approximation of solutions to the direct kinematics problem of parallel manipulators

2020 ◽  
Vol 10 (1) ◽  
pp. 65-70
Author(s):  
Andrei Gorchakov ◽  
Vyacheslav Mozolenko
Keyword(s):  
Neural Network ◽  
Bounded Function ◽  
Parallel Manipulators ◽  
Feedforward Neural Network ◽  
Numerical Implementation ◽  
Continuous Bounded Function ◽  
Space Filling ◽  
Space Filling Curves ◽  
Direct Kinematics

AbstractAny real continuous bounded function of many variables is representable as a superposition of functions of one variable and addition. Depending on the type of superposition, the requirements for the functions of one variable differ. The article investigated one of the options for the numerical implementation of such a superposition proposed by Sprecher. The superposition was presented as a three-layer Feedforward neural network, while the functions of the first’s layer were considered as a generator of space-filling curves (Peano curves). The resulting neural network was applied to the problems of direct kinematics of parallel manipulators.

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