scholarly journals Real-time RSS-based positioning system using neural network algorithm

2021 ◽  
Vol 22 (3) ◽  
pp. 1601
Author(s):  
Safae El Abkari ◽  
Jamal El Mhamdi ◽  
El Hassan El Abkari
Keyword(s):  
Neural Network ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Grid Size ◽  
Average Error ◽  
Positioning System ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Neural Network Algorithm ◽  
Locating System

Locating services have come under the spotlight in recent years in various applications. However, locating methods that use received signal strength have low accuracy due to signal fluctuations. For this purpose, we present a Wi-Fi based locating system using artificial neural network to enhance the positioning process performances. We optimized the Levenberg Marquardt algorithm to propose the better configuration of the multi-layer time-delay perception neural network. We achieved an average error of 10.3 centimeters with a grid of 0.4 meter in four tests. Yet, due to the instability of the received signal strength RSS-based locating systems present a limitation in the resolution finesse that depends on the grid size.

Improved trilateration for indoor localization: Neural network and centroid-based approach

2021 ◽  
Vol 17 (11) ◽  
pp. 155014772110539
Author(s):  
Satish R Jondhale ◽  
Amruta S Jondhale ◽  
Pallavi S Deshpande ◽  
Jaime Lloret
Keyword(s):  
Neural Network ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Iteration Step ◽  
Generalized Regression Neural Network ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Generalized Regression

Location awareness is the key to success to many location-based services applications such as indoor navigation, elderly tracking, emergency management, and so on. Trilateration-based localization using received signal strength measurements is widely used in wireless sensor network–based localization and tracking systems due to its simplicity and low computational cost. However, localization accuracy obtained with the trilateration technique is generally very poor because of fluctuating nature of received signal strength measurements. The reason behind such notorious behavior of received signal strength is dynamicity in target motion and surrounding environment. In addition, the significant localization error is induced during each iteration step during trilateration, which gets propagated in the next iterations. To address this problem, this article presents an improved trilateration-based architecture named Trilateration Centroid Generalized Regression Neural Network. The proposed Trilateration Centroid Generalized Regression Neural Network–based localization algorithm inherits the simplicity and efficiency of three concepts namely trilateration, centroid, and Generalized Regression Neural Network. The extensive simulation results indicate that the proposed Trilateration Centroid Generalized Regression Neural Network algorithm demonstrates superior localization performance as compared to trilateration, and Generalized Regression Neural Network algorithm.

scholarly journals The atmospheric model of neural networks based on the improved Levenberg-Marquardt algorithm

2021 ◽  
Vol 30 (1) ◽  
pp. 24-35
Author(s):  
Wenhui Cui ◽  
Wei Qu ◽  
Min Jiang ◽  
Gang Yao
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Influencing Factor ◽  
Atmospheric Model ◽  
Average Error ◽  
Atmospheric Models ◽  
Neural Network Models ◽  
Model Based ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt

Abstract Traditional atmospheric models are based on the analysis and fitting of various factors influencing the space atmosphere density. Neural network models do not specifically analyze the polynomials of each influencing factor in the atmospheric model, but use large data sets for network construction. Two traditional atmospheric model algorithms are analyzed, the main factors affecting the atmospheric model are identified, and an atmospheric model based on neural networks containing various influencing factors is proposed. According to the simulation error, the Levenberg-Marquardt algorithm is used to iteratively realize the rapid network weight correction, and the optimal neural network atmospheric model is obtained. The space atmosphere is simulated and calculated with an atmospheric model based on neural networks, and its average error rate is lower than that of traditional atmospheric models such as the DTM2013 model and the MSIS00 model. At the same time, the calculation complexity of the atmospheric model based on the neural networks is significantly simplified than that of the traditional atmospheric model.

Comparison of an Artificial Neural Network and a Multiple Linear Regression in Predicting the Heat of Combustion of Diesel Fuel Based on Hydrocarbon Groups

2020 ◽  
Vol 71 (6) ◽  
pp. 66-74
Author(s):  
Younis M. Younis ◽  
Salman H. Abbas ◽  
Farqad T. Najim ◽  
Firas Hashim Kamar ◽  
Gheorghe Nechifor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Fuel ◽  
Back Propagation ◽  
Second Step ◽  
Heating Value ◽  
Network Training ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Propagation Network

A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data.

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