scholarly journals DEVELOPMENT OF ARTIFICIAL NEURAL NETWORK STRUCTURES FOR PREDICTING NAVIGATION TASKS OF A MOBILE ROBOT

2020 ◽  
Vol 7 (3) ◽  
pp. 42-50
Author(s):  
Irem Sahmutoglu ◽  
Erhan AKDOGAN
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mobile Robot ◽  
Trajectory Optimization ◽  
Mobile Robot Navigation ◽  
Network Structures ◽  
Multi Layer Perceptron ◽  
Simulation Environment ◽  
Network Output ◽  
Artificial Neural

Determining trajectories in mobile robot navigation tasks is a difficult process to apply with conventional methods. Therefore, intelligent techniques produce highly effective results in trajectory optimization and orientation prediction. In this study, two different ANN (Artificial Neural Network) structures have been developed for the navigation prediction of the SCITOS G5 mobile robot. For this aim, RBF (Radial Basis Function) and MLP (Multi-Layer Perceptron) structures were used. Information obtained from 24 sensors of the robot was used as network inputs and network output determines robot direction. Accordingly, structures that have 24 inputs and one output were created. The best performance network structures obtained were compared among them in simulation environment. Accordingly, RBF has been observed to produce more accurate results than MLP.

Speckle Noise Filtering Using Back-Propagation Multi-Layer Perceptron Network in Synthetic Aperture Radar Image

2020 ◽  
pp. 489-510
Author(s):  
Khwairakpam Amitab ◽  
Debdatta Kandar ◽  
Arnab K. Maji
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Synthetic Aperture Radar ◽  
Back Propagation ◽  
Speckle Noise ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Multi Layer Perceptron ◽  
Sar Images ◽  
Artificial Neural

Synthetic Aperture Radar (SAR) are imaging Radar, it uses electromagnetic radiation to illuminate the scanned surface and produce high resolution images in all-weather condition, day and night. Interference of signals causes noise and degrades the quality of the image, it causes serious difficulty in analyzing the images. Speckle is multiplicative noise that inherently exist in SAR images. Artificial Neural Network (ANN) have the capability of learning and is gaining popularity in SAR image processing. Multi-Layer Perceptron (MLP) is a feed forward artificial neural network model that consists of an input layer, several hidden layers, and an output layer. We have simulated MLP with two hidden layer in Matlab. Speckle noises were added to the target SAR image and applied MLP for speckle noise reduction. It is found that speckle noise in SAR images can be reduced by using MLP. We have considered Log-sigmoid, Tan-Sigmoid and Linear Transfer Function for the hidden layers. The MLP network are trained using Gradient descent with momentum back propagation, Resilient back propagation and Levenberg-Marquardt back propagation and comparatively evaluated the performance.

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