Application of an artificial neural network as a flight test data estimator

1995 ◽  
Author(s):  
Roger McMillen ◽  
James Steck ◽  
Kamran Rokhsaz
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Test Data ◽  
Flight Test ◽  
Artificial Neural

Application of an artificial neural network as a flight test data estimator

1995 ◽  
Vol 32 (5) ◽  
pp. 1088-1094 ◽  
Author(s):  
Roger L. McMillen ◽  
James E. Steck ◽  
Kamran Rokhsaz
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Test Data ◽  
Flight Test ◽  
Artificial Neural

Nonlinear auto-regressive neural network for mathematical modelling of an airship using experimental data

2018 ◽  
Vol 233 (7) ◽  
pp. 2549-2569
Author(s):  
Farrukh Mazhar ◽  
Mohammad A Choudhry ◽  
Muhammad Shehryar
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Flight Test ◽  
Aerial Vehicle ◽  
Auto Regressive ◽  
Artificial Neural ◽  
Trained Neural Network

Autonomous flight of an aerial vehicle requires a sufficiently accurate mathematical model, which can capture system dynamics in the presence of external disturbances. Artificial neural network is known for ideal in capturing systems behaviour, where little knowledge about vehicle dynamics is available. In this paper, we explored this potential of artificial neural network for characterizing nonlinear dynamics of an unmanned airship. The flight experimentation data for an outdoor experimental airship are acquired through a series of pre-determined flight tests. The experimental data are subjected to a class of dynamic recurrent neural network model dubbed as nonlinear auto-regressive model with exogenous inputs for training. Sufficiently trained neural network model captured and demonstrated the longitudinal dynamics of the airship satisfactorily. We also demonstrated the usefulness of proposed technique for Lotte airship, wherein the performance of proposed model is validated and analysed for the Lotte airship flight test data.

Prediction of Resilient Modulus for Hot Mix Asphalt Based on Artificial Neural Network

2011 ◽  
Vol 304 ◽  
pp. 18-23
Author(s):  
Chun Hua Hu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Test Data ◽  
Hot Mix Asphalt ◽  
Resilient Modulus ◽  
Structure Design ◽  
Forecast Precision ◽  
Long Time ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Resilient modulus of material is an important parameter for pavement structure design and analysis. However it is very tedious to get this parameter for hot mixture asphalt in laboratory. Moreover it takes long time to do experiments. In this paper, artificial neural network (ANN) is applied to predict to resilient modulus for hot mixture asphalt. A neural network model is constructed and trained plenty of times with selected test data until precision meets requirement. Then the model is used to predict resilient modulus for hot mix asphalt. Result of contrast prediction with test data shows that forecast precision is high. This provides a new method to predict resilient modulus for hot mixture asphalt.

Simulation of Performance Deterioration of a Microturbine and Application of Neural Network to Its Performance Diagnosis

2008 ◽  
Author(s):  
Jae Eun Yoon ◽  
Jong Joon Lee ◽  
Tong Seop Kim ◽  
Jeong Lak Sohn
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Test Data ◽  
Input Data ◽  
Data Sets ◽  
Characteristic Parameters ◽  
Performance Diagnosis ◽  
The Neural Network ◽  
Performance Deterioration ◽  
Artificial Neural

This study aims to simulate performance deterioration of a microturbine and apply artificial neural network to its performance diagnosis. As it is hard to obtain test data with degraded component performance, the degraded engine data have been acquired through simulation. Artificial neural network is adopted as the diagnosis tool. First, the microturbine has been tested to get reference operation data, assumed to be degradation free. Then, a simulation program was set up to regenerate the performance test data. Deterioration of each component (compressor, turbine and recuperator) was modeled by changes in the component characteristic parameters such as compressor and turbine efficiency, their flow capacities and recuperator effectiveness and pressure drop. Single and double faults (deterioration of single and two components) were simulated to generate fault data. The neural network was trained with majority of the data sets. Then, the remaining data sets were used to check the predictability of the neural network. Given measurable performance parameters (power, temperatures, pressures) as inputs to the neural network, characteristic parameters of each component were predicted as outputs and compared with original data. The neural network produced sufficiently accurate prediction. Reducing the number of input data decreased prediction accuracy. However, excluding up to a couple of input data still produced acceptable accuracy.

scholarly journals Artificial Neural Networks as Applied to Aircraft Flight Test Planning / Wykorzystanie Sztucznych Sieci Neuronowych W Planowaniu Badań W Locie

2012 ◽  
Vol 21 (1) ◽  
pp. 149-162
Author(s):  
Mirosław Zieja ◽  
Joanna Wójcik
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flight Test ◽  
Test Plan ◽  
Military Aircraft ◽  
Test Program ◽  
Aircraft Flight ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Military

Abstract Any flight test programme is essentially based on a flight test plan, i.e. a document agreed upon and accepted by all the parties interested in the tests, and authorised by the superior body/authorities responsible for the execution of the tests. The flight test plan determines the number of flights and flight hours indispensable to verify whether a given aircraft satisfies specified requirements. Many and various external factors may have significant and adverse effect on the execution of the flight test program according to the earlier agreed schedule. The study also covers the structure of a model of a management system for aircraft prototype testing, and the structure of artificial neural network (ANN) developed on the basis of experimentally gained data from the military aircraft testing. A mathematical model based on the artificial neural network and its potential for the managing of aircraft prototypes testing has been formulated as well.

scholarly journals Neuron Network Modeling of Intensification of Isogumulone Extraction in a Rotary Pulse Generator

2021 ◽  
pp. 593-603
Author(s):  
Anton Shafrai ◽  
Elena Safonova ◽  
Dmitry Borodulin ◽  
Yana Golovacheva ◽  
Sergey Ratnikov ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Regression Model ◽  
Test Data ◽  
Pulse Generator ◽  
Processing Temperature ◽  
Neuron Network ◽  
Artificial Neural

Introduction. Artificial neural networks are a popular tool of contemporary research and technology, including food science, where they can be used to model various technological processes. The present research objective was to develop an artificial neural network capable of predicting the content of isogumulone in a hop extract at given technological parameters of the rotary pulse generator. Study objects and methods. The mathematical modeling was based on experimental data. The isogumulone content in the hop extract I (mg/dm3) served as an output parameter. The input variables included: processing temperature t (°C), rotor speed n (rpm), processing time  (min), and the gap between the rotor teeth and stator s (mm). Results and discussion. The resulting model had the following parameters: two hidden layers, 30 neurons each; neuron activation function – GELU; loss function – MSELoss; learning step – 0.001; optimizer – Adam; L2 regularization at 0.00001; training set of four batches, 16 records each; 9,801 epochs. The accuracy of the artificial neural network (1.67%) was defined as the mean relative error. The error of the regression model was also low (2.85%). The neural network proved to be more accurate than the regression model and had a better ability to predict the value of the output variable. The accuracy of the artificial neural network was higher because it used test data not included in the training. The regression model when tested on test data showed much worse results. Conclusion. Artificial neural networks proved extremely useful as a means of technological modeling and require further research and application.

scholarly journals Predictions of coronavirus COVID-19 distinct cases in Pakistan through an artificial neural network

2020 ◽  
Vol 148 ◽  
Author(s):  
Iftikhar Ahmad ◽  
Syed Muhammad Asad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Test Data ◽  
Research Study ◽  
Main Motivation ◽  
Major Threat ◽  
Artificial Neural ◽  
And Control

Abstract This study presents the main motivation to investigate the COVID-19 pandemic, a major threat to the whole world from the day when it first emerged in China city of Wuhan. Predictions on the number of cases of COVID-19 are crucial in order to prevent and control the outbreak. In this research study, an artificial neural network with rectifying linear unit-based technique is implemented to predict the number of deaths, recovered and confirmed cases of COVID-19 in Pakistan by using previous data of 137 days of COVID-19 cases from the day 25 February 2020 when the first two cases were confirmed, until 10 July 2020. The collected data were divided into training and test data which were used to test the efficiency of the proposed technique. Furthermore, future predictions have been made by the proposed technique for the next 7 days while training the model on whole available data.

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