Design and Implementation of an Artificial Neural Network Wavelet for Load Transportation with two Unmanned Aircraft Systems

2019 ◽  
Author(s):  
C. E. Juarez Vargas ◽  
J. Suarez Cansino ◽  
E. S. Espinoza Quesada ◽  
L. R. Garcia Carrillo ◽  
L. E. Ramos-Velasco ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Unmanned Aircraft ◽  
Unmanned Aircraft Systems ◽  
Design And Implementation ◽  
Aircraft Systems ◽  
Artificial Neural ◽  
Load Transportation

scholarly journals Identification Of Number Using Artificial Neural Network Backpropagation

2018 ◽  
Vol 215 ◽  
pp. 01011
Author(s):  
Sitti Amalia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Training Data ◽  
Simulation Test ◽  
Backpropagation Algorithm ◽  
Network Parameter ◽  
Artificial Intelligent ◽  
Design And Implementation ◽  
Network Training ◽  
Artificial Neural

This research proposed to design and implementation system of voice pattern recognition in the form of numbers with offline pronunciation. Artificial intelligent with backpropagation algorithm used on the simulation test. The test has been done to 100 voice files which got from 10 person voices for 10 different numbers. The words are consisting of number 0 to 9. The trial has been done with artificial neural network parameters such as tolerance value and the sum of a neuron. The best result is shown at tolerance value varied and a sum of the neuron is fixed. The percentage of this network training with optimal architecture and network parameter for each training data and new data are 82,2% and 53,3%. Therefore if tolerance value is fixed and a sum of neuron varied gave 82,2% for training data and 54,4% for new data

scholarly journals Intelligent Control for Unmanned Aerial Systems with System Uncertainties and Disturbances Using Artificial Neural Network

Drones ◽  
2018 ◽  
Vol 2 (3) ◽  
pp. 30 ◽  
Author(s):  
Mohammad Jafari ◽  
Hao Xu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intelligent Control ◽  
Unmanned Aircraft ◽  
Nonlinear Controller ◽  
Adaptive Neural Network ◽  
Intelligent Controller ◽  
Neural Network Controller ◽  
System Uncertainties ◽  
Artificial Neural

Stabilizing the Unmanned Aircraft Systems (UAS) under complex environment including system uncertainties, unknown noise and/or disturbance is so challenging. Therefore, this paper proposes an adaptive neural network based intelligent control method to overcome these challenges. Based on a class of artificial neural network, named Radial Basis Function (RBF) networks an adaptive neural network controller is designed. To handle the unknown dynamics and uncertainties in the system, firstly, we develop a neural network based identifier. Then, a neural network based controller is generated based on both the identified model of the system and the linear or nonlinear controller. To ensure the stability of the system during its online training phase, the linear or nonlinear controller is utilized. The learning capability of the proposed intelligent controller makes it a promising approach to take system uncertainties, noises and/or disturbances into account. The satisfactory performance of the proposed intelligent controller is validated based on the computer based simulation results of a benchmark UAS with system uncertainties and disturbances, such as wind gusts disturbance.

scholarly journals Construction of an advanced method for recognizing monitored objects by a convolutional neural network using a discrete wavelet transform

2021 ◽  
Vol 4 (9(112)) ◽  
pp. 65-77
Author(s):  
Vadym Slyusar ◽  
Mykhailo Protsenko ◽  
Anton Chernukha ◽  
Stella Gornostal ◽  
Sergey Rudakov ◽  
...  
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Discrete Wavelet Transform ◽  
Unmanned Aircraft ◽  
Unmanned Aircraft Systems ◽  
Discrete Wavelet ◽  
Surveillance Systems ◽  
Improved Method ◽  
Aircraft Systems

The tasks that unmanned aircraft systems solve include the detection of objects and determining their state. This paper reports an analysis of image recognition methods in order to automate the specified process. Based on the analysis, an improved method for recognizing images of monitored objects by a convolutional neural network using a discrete wavelet transform has been devised. Underlying the method is the task of automating image processing in unmanned aircraft systems. The operability of the proposed method was tested using an example of processing an image (aircraft, tanks, helicopters) acquired by the optical system of an unmanned aerial vehicle. A discrete wavelet transform has been used to build a database of objects' wavelet images and train a convolutional neural network based on them. That has made it possible to improve the efficiency of recognition of monitored objects and automate a given process. The effectiveness of the improved method is achieved by preliminary decomposition and approximation of the digital image of the monitored object by a discrete wavelet transform. The stages of a given method include the construction of a database of the wavelet images of images and training a convolutional neural network. The effectiveness of recognizing the monitored objects' images by the improved method was tested on a convolutional neural network, which was trained with images of 300 monitored objects. In this case, the time to make a decision, based on the proposed method, decreased on average from 0.7 to 0.84 s compared with the artificial neural networks ResNet and ConvNets. The method could be used in the information processing systems in unmanned aerial vehicles that monitor objects; in robotic complexes for various purposes; in the video surveillance systems of important objects

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