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.

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.

scholarly journals Adaptive Levenberg–Marquardt Algorithm: A New Optimization Strategy for Levenberg–Marquardt Neural Networks

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2176
Author(s):  
Zhiqi Yan ◽  
Shisheng Zhong ◽  
Lin Lin ◽  
Zhiquan Cui
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Frequency Noise ◽  
Optimization Strategy ◽  
Engineering Data ◽  
Marquardt Algorithm ◽  
Highly Nonlinear ◽  
Rectified Linear Unit ◽  
Levenberg Marquardt ◽  
Lm Algorithm

Engineering data are often highly nonlinear and contain high-frequency noise, so the Levenberg–Marquardt (LM) algorithm may not converge when a neural network optimized by the algorithm is trained with engineering data. In this work, we analyzed the reasons for the LM neural network’s poor convergence commonly associated with the LM algorithm. Specifically, the effects of different activation functions such as Sigmoid, Tanh, Rectified Linear Unit (RELU) and Parametric Rectified Linear Unit (PRLU) were evaluated on the general performance of LM neural networks, and special values of LM neural network parameters were found that could make the LM algorithm converge poorly. We proposed an adaptive LM (AdaLM) algorithm to solve the problem of the LM algorithm. The algorithm coordinates the descent direction and the descent step by the iteration number, which can prevent falling into the local minimum value and avoid the influence of the parameter state of LM neural networks. We compared the AdaLM algorithm with the traditional LM algorithm and its variants in terms of accuracy and speed in the context of testing common datasets and aero-engine data, and the results verified the effectiveness of the AdaLM algorithm.

The Optimal Levenberg-Marquardt Algorithm to Predict Welding Quality Using Mahalanobis Distance Theory

2014 ◽  
Vol 936 ◽  
pp. 1873-1877
Author(s):  
Ill Soo Kim ◽  
Qian Qian Wu ◽  
Ji Hye Lee ◽  
Jong Pyo Lee ◽  
Min Ho Park ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mahalanobis Distance ◽  
Gma Welding ◽  
Welding Process ◽  
Welding Parameters ◽  
Welding Quality ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
On Line

With the development of computational technology, neural network has attracted the more and more attentions to reveal the relationships between the process parameters and welding geometry. However, the Gas Metal Arc (GMA) welding is complex and of multiple interactions so that mathematical model for welding parameters has not been achieved. Neural networks have been noted as being particularly advantageous for modeling systems which contain noisy, fuzzy and uncertain elements, while a sufficient algorithm is employed. In this study, Levenberg-Marquardt algorithm was employed into GMA welding process. Mahalanobis Distance (MD) was measured to determine the on-line welding quality to avoid joint failure as welding quality. To get an optimal neural network, cases with different configurations were carried out. The Root of the Mean sum of Squared (RMS) error was adopted to evaluate the accuracy of the prediction by neural networks with LM algorithm. The results presented that the proposed algorithm had the superiority of high accuracy that can be used in the on-line welding process.

Neural Network Techniques for Time Series Prediction: A Review

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Muhammad Faheem Mushtaq ◽  
Urooj Akram ◽  
Muhammad Aamir ◽  
Haseeb Ali ◽  
Muhammad Zulqarnain
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Data ◽  
Weather Prediction ◽  
Time Series Prediction ◽  
Series Data ◽  
Prediction Problem ◽  
Neural Network Models ◽  
Physical Time

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series.  Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

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.

Logistics demand forecasting model based on improved neural network algorithm

2020 ◽  
pp. 1-11
Author(s):  
Hongjiang Ma ◽  
Xu Luo
Keyword(s):  
Neural Network ◽  
Influencing Factor ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Logistics System ◽  
Model Based ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Demand Forecasting Model ◽  
Logistics Demand

The irrationality between the procurement and distribution of the logistics system increases unnecessary circulation links and greatly reduces logistics efficiency, which not only causes a waste of transportation resources, but also increases logistics costs. In order to improve the operation efficiency of the logistics system, based on the improved neural network algorithm, this paper combines the logistic regression algorithm to construct a logistics demand forecasting model based on the improved neural network algorithm. Moreover, according to the characteristics of the complexity of the data in the data mining task itself, this article optimizes the ladder network structure, and combines its supervisory decision-making part with the shallow network to make the model more suitable for logistics demand forecasting. In addition, this paper analyzes the performance of the model based on examples and uses the grey relational analysis method to give the degree of correlation between each influencing factor and logistics demand. The research results show that the model constructed in this paper is reasonable and can be analyzed from a practical perspective.

scholarly journals Opening the black box of neural networks: methods for interpreting neural network models in clinical applications

2018 ◽  
Vol 6 (11) ◽  
pp. 216-216 ◽  
Author(s):  
Zhongheng Zhang ◽  
◽  
Marcus W. Beck ◽  
David A. Winkler ◽  
Bin Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Models ◽  
Black Box ◽  
Clinical Applications ◽  
Neural Network Models
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