Adjustment and Analysis of Time Delay in Active Control of Structure Based on Artificial Neural Network

In the paper, visual navigation of a drone is considered. The drone navigation problem consists of two parts. The first part is finding the real position and orientation of the drone. The second part is finding the difference between desirable and real position and orientation of the drone and creation of the correspondent control signal for decreasing the difference. For the first part of the drone navigation problem, the paper presents a method for determining the coordinates of the drone camera with respect to known three-dimensional (3D) ground objects using deep learning. The algorithm has two stages. It causes the algorithm to be easy for interpretation by artificial neural network (ANN) and consequently increases its accuracy. At the first stage, we use the first ANN to find coordinates of the object origin projection. At the second stage, we use the second ANN to find the drone camera position and orientation. The algorithm has high accuracy (these errors were found for the validation set of images as differences between positions and orientations, obtained from a pretrained artificial neural network, and known positions and orientations), it is not sensitive to interference associated with changes in lighting, the appearance of external moving objects and the other phenomena where other methods of visual navigation are not effective. For the second part of the drone navigation problem, the paper presents a method for stabilization of drone flight controlled by autopilot with time delay. Indeed, image processing for navigation demands a lot of time and results in a time delay. However, the proposed method allows to get stable control in the presence of this time delay.

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A time delay artificial neural network approach for flow routing in a river system

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-3-2735-2006 ◽

2006 ◽

Vol 3 (5) ◽

pp. 2735-2756 ◽

Cited By ~ 4

Author(s):

M. J. Diamantopoulou ◽

P. E. Georgiou ◽

D. M. Papamichail

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Time Delay ◽

Cross Correlation ◽

River Flow ◽

Flow Routing ◽

Wide Range ◽

Input Layer ◽

Artificial Neural ◽

Time Lagged

Abstract. River flow routing provides basic information on a wide range of problems related to the design and operation of river systems. In this paper, three layer cascade correlation Time Delay Artificial Neural Network (TDANN) models have been developed to forecast the one day ahead daily flow at Ilarionas station on the Aliakmon river, in Northern Greece. The networks are time lagged feed-formatted with delayed memory processing elements at the input layer. The network topology is using multiple inputs, which include the time lagged daily flow values further up at Siatista station on the Aliakmon river and at Grevena station on the Venetikos river, which is a tributary to the Aliakmon river and a single output, which are the daily flow values at Ilarionas station. The choice of the input variables introduced to the input layer was based on the cross-correlation. The use of cross-correlation between the ith input series and the output provides a short cut to the problem of the delayed memory determination. Kalman's learning rule was used to modify the artificial neural network weights. The networks are designed by putting weights between neurons, by using the hyperbolic-tangent function for training. The number of nodes in the hidden layer was determined based on the maximum value of the correlation coefficient. The results show a good performance of the TDANN approach for forecasting the daily flow values, at Ilarionas station and demonstrate its adequacy and potential for river flow routing. The TDANN approach introduced in this study is sufficiently general and has great potential to be applicable to many hydrological and environmental applications.

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Time–delay single layer artificial neural network models for estimating shelf life of burfi

International Journal of Research Studies in Computing ◽

10.5861/ijrsc.2012.101 ◽

2012 ◽

Vol 1 (2) ◽

Author(s):

Sumit Goyal ◽

Gyanendra Kumar Goyal

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Time Delay ◽

Shelf Life ◽

Single Layer ◽

Network Models ◽

Neural Network Models ◽

Artificial Neural ◽

Artificial Neural Network Models

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SIGNATURE VERIFICATION USING A “SIAMESE” TIME DELAY NEURAL NETWORK

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001493000339 ◽

1993 ◽

Vol 07 (04) ◽

pp. 669-688 ◽

Cited By ~ 321

Author(s):

JANE BROMLEY ◽

JAMES W. BENTZ ◽

LÉON BOTTOU ◽

ISABELLE GUYON ◽

YANN LECUN ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Time Delay ◽

System Performance ◽

Feature Vector ◽

Signature Verification ◽

The Novel ◽

Verification Algorithm ◽

Siamese Network ◽

Artificial Neural

This paper describes the development of an algorithm for verification of signatures written on a touch-sensitive pad. The signature verification algorithm is based on an artificial neural network. The novel network presented here, called a “Siamese” time delay neural network, consists of two identical networks joined at their output. During training the network learns to measure the similarity between pairs of signatures. When used for verification, only one half of the Siamese network is evaluated. The output of this half network is the feature vector for the input signature. Verification consists of comparing this feature vector with a stored feature vector for the signer. Signatures closer than a chosen threshold to this stored representation are accepted, all other signatures are rejected as forgeries. System performance is illustrated with experiments performed in the laboratory.

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Surface Electrocardiogram Reconstruction From Intracardiac Electrograms Using a Dynamic Time Delay Artificial Neural Network

IEEE Transactions on Biomedical Engineering ◽

10.1109/tbme.2012.2225428 ◽

2013 ◽

Vol 60 (1) ◽

pp. 106-114 ◽

Cited By ~ 3

Author(s):

Fabienne Porée ◽

Amar Kachenoura ◽

Guy Carrault ◽

Renzo Dal Molin ◽

Philippe Mabo ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Time Delay ◽

Artificial Neural ◽

Dynamic Time ◽

Surface Electrocardiogram

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Switched time delay control based on artificial neural network for fault detection and compensation in robot manipulators

SN Applied Sciences ◽

10.1007/s42452-021-04376-z ◽

2021 ◽

Vol 3 (4) ◽

Author(s):

Dihya Maincer ◽

Moufid Mansour ◽

Amar Hamache ◽

Chemseddine Boudjedir ◽

Moussaab Bounabi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Time Delay ◽

Control Method ◽

High Gain ◽

Industrial Applications ◽

Control Laws ◽

Time Delay Control ◽

Delay Control ◽

Artificial Neural

AbstractThis work proposes a switched time delay control scheme based on neural networks for robots subjected to sensors faults. In this scheme, a multilayer perceptron (MLP) artificial neural network (ANN) is introduced to reproduce the same behavior of a robot in the case of no faults. The reproduction characteristic of the MLPs allows instant detection of any important sensor faults. In order to compensate the effects of these faults on the robot’s behavior, a time delay control (TDC) procedure is presented. The proposed controller is composed of two control laws: The first one contains a small gain applied to the faultless robot, while the second scheme uses a high gain that is applied to the robot subjected to faults. The control method applied to the system is decided based on the ANN detection results which switches from the first control law to the second one in the case where an important fault is detected. Simulations are performed on a SCARA arm manipulator to illustrate the feasibility and effectiveness of the proposed controller. The results demonstrate that the free-model aspect of the proposed controller makes it highly suitable for industrial applications.

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