scholarly journals An Intelligent Lightning Warning System Based on Electromagnetic Field and Neural Network

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1275 ◽  
Author(s):  
Guoming Wang ◽  
Woo-Hyun Kim ◽  
Gyung-Suk Kil ◽  
Dae-Won Park ◽  
Sung-Wook Kim
Keyword(s):  
Neural Network ◽  
Electromagnetic Field ◽  
Electric Field ◽  
Prediction Accuracy ◽  
Back Propagation ◽  
Warning System ◽  
Back Propagation Algorithm ◽  
Threshold Method ◽  
Lightning Strikes ◽  
Loop Antennas

Prediction of lightning occurrence has significant relevance for reducing potential damage to electric installations, buildings, and humans. However, the existing lightning warning system (LWS) operates using the threshold method and has low prediction accuracy. In this paper, an intelligent LWS based on an electromagnetic field and the artificial neural network was developed for improving lightning prediction accuracy. An electric field mill sensor and a pair of loop antennas were designed to detect the real-time electric field and the magnetic field induced by lightning, respectively. The change rate of electric field, temperature, and humidity acquired 2 min before lightning strikes, were used for developing the neural network using the back propagation algorithm. After observing and predicting lightning strikes over six months, it was verified that the proposed LWS had a prediction accuracy of 93.9%.

scholarly journals Deep neural networks using a single neuron: folded-in-time architecture using feedback-modulated delay loops

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Florian Stelzer ◽  
André Röhm ◽  
Raul Vicente ◽  
Ingo Fischer ◽  
Serhiy Yanchuk
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Network ◽  
Single Neuron ◽  
Deep Neural Networks ◽  
Back Propagation ◽  
Local Network ◽  
Multiple Time ◽  
Learning Tools ◽  
Back Propagation Algorithm

AbstractDeep neural networks are among the most widely applied machine learning tools showing outstanding performance in a broad range of tasks. We present a method for folding a deep neural network of arbitrary size into a single neuron with multiple time-delayed feedback loops. This single-neuron deep neural network comprises only a single nonlinearity and appropriately adjusted modulations of the feedback signals. The network states emerge in time as a temporal unfolding of the neuron’s dynamics. By adjusting the feedback-modulation within the loops, we adapt the network’s connection weights. These connection weights are determined via a back-propagation algorithm, where both the delay-induced and local network connections must be taken into account. Our approach can fully represent standard Deep Neural Networks (DNN), encompasses sparse DNNs, and extends the DNN concept toward dynamical systems implementations. The new method, which we call Folded-in-time DNN (Fit-DNN), exhibits promising performance in a set of benchmark tasks.

scholarly journals Using Artificial Neural Networks in Predicting the Level of Stress among Military Conscripts

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 626
Author(s):  
Svajone Bekesiene ◽  
Rasa Smaliukiene ◽  
Ramute Vaicaitiene
Keyword(s):  
Neural Network ◽  
Transfer Functions ◽  
Back Propagation ◽  
Armed Forces ◽  
Activation Function ◽  
Optimal Number ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Classification Rate ◽  
Artificial Neural

The present study aims to elucidate the main variables that increase the level of stress at the beginning of military conscription service using an artificial neural network (ANN)-based prediction model. Random sample data were obtained from one battalion of the Lithuanian Armed Forces, and a survey was conducted to generate data for the training and testing of the ANN models. Using nonlinearity in stress research, numerous ANN structures were constructed and verified to limit the optimal number of neurons, hidden layers, and transfer functions. The highest accuracy was obtained by the multilayer perceptron neural network (MLPNN) with a 6-2-2 partition. A standardized rescaling method was used for covariates. For the activation function, the hyperbolic tangent was used with 20 units in one hidden layer as well as the back-propagation algorithm. The best ANN model was determined as the model that showed the smallest cross-entropy error, the correct classification rate, and the area under the ROC curve. These findings show, with high precision, that cohesion in a team and adaptation to military routines are two critical elements that have the greatest impact on the stress level of conscripts.

COMPUTING AN ADAPTIVE MESH IN FLUID PROBLEMS USING NEURAL NETWORK AND GENETIC ALGORITHM WITH ADAPTIVE RELAXATION

2008 ◽  
Vol 17 (06) ◽  
pp. 1089-1108 ◽  
Author(s):  
NAMEER N. EL. EMAM ◽  
RASHEED ABDUL SHAHEED
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Numerical Experiments ◽  
Back Propagation ◽  
Adaptive Mesh ◽  
Learning System ◽  
Back Propagation Algorithm ◽  
Adaptive Smoothing ◽  
Different Types ◽  
Optimal Values

A method based on neural network with Back-Propagation Algorithm (BPA) and Adaptive Smoothing Errors (ASE), and a Genetic Algorithm (GA) employing a new concept named Adaptive Relaxation (GAAR) is presented in this paper to construct learning system that can find an Adaptive Mesh points (AM) in fluid problems. AM based on reallocation scheme is implemented on different types of two steps channels by using a three layer neural network with GA. Results of numerical experiments using Finite Element Method (FEM) are discussed. Such discussion is intended to validate the process and to demonstrate the performance of the proposed learning system on three types of two steps channels. It appears that training is fast enough and accurate due to the optimal values of weights by using a few numbers of patterns. Results confirm that the presented neural network with the proposed GA consistently finds better solutions than the conventional neural network.

Study on the Generalized Holo-Factors Mathematical Model of Dimension-Error and Shape-Error for Sheet Metal in Stamping Based on the Back Propagation (BP) Neural Network

2016 ◽  
Vol 138 (6) ◽  
Author(s):  
Lizhi Gu ◽  
Tianqing Zheng
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Sheet Metal ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Shape Error ◽  
Back Propagation Algorithm ◽  
Minor Strain ◽  
Major Strain ◽  
Forming Precision

Precision improvement in sheet metal stamping has been the concern that the stamping researchers have engaged in. In order to improve the forming precision of sheet metal in stamping, this paper devoted to establish the generalized holo-factors mathematical model of dimension-error and shape-error for sheet metal in stamping based on BP neural network. Factors influencing the forming precision of stamping sheet metal were divided, altogether ten factors, and the generalized holo-factors mathematical model of dimension-error and shape-error for sheet metal in stamping was established using the back-propagation algorithm of error based on BP neural network. The undetermined coefficients of the model previously established were soluble according to the simulation data of sheet punching combined with the specific shape based on the BP neural network. With this mathematical model, the forecast data compared with the validate data could be obtained, so as to verify the fine practicability that the previously established mathematical model had, and then, it was shown that the generalized holo-factors mathematical model of size error and shape-error had fine practicality and versatility. Based on the generalized holo-factors mathematical model of error exemplified by the cylindrical parts, a group of process parameters could be selected, in which forming thickness was between 0.713 mm and 1.335 mm, major strain was between 0.085 and 0.519, and minor strain was between −0.596 and 0.319 from the generalized holo-factors mathematical model prediction, at the same time, the forming thickness, the major strain, and the minor strain were in good condition.

Research of the 3-DOF Helicopter System Based on Adaptive Inverse Control

2013 ◽  
Vol 389 ◽  
pp. 623-631 ◽  
Author(s):  
Xiu Yan Wang ◽  
Ying Wang ◽  
Zong Shuai Li
Keyword(s):  
Neural Network ◽  
Flight Control ◽  
Fuzzy Inference ◽  
Reference Model ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Actual System ◽  
Adaptive Inverse Control ◽  
Inverse Control ◽  
Network Connection

For the flight control problem occurred in 3-DOF Helicopter System, reference adaptive inverse control scheme based on Fuzzy Neural Network model is designed. Firstly, fuzzy inference process of identifier and controller is achieved by using the network structure. Meanwhile, the neural network connection weights are used to express parameters of fuzzy inference. Then, back-propagation algorithm is adopted to amend the network connection weights in order to automatically identify the fuzzy model and adjust its membership functions and parameters, so that the actual system output of adaptive inverse controller control which is adjusted can track the reference model output. Finally, the simulation result of 3-DOF Helicopter System based on the scheme shows that the method is effective and feasible.

Test on Flood Prediction-Model Using Artificial Neural Network for ShiiLiAn Hydrologic Station on MinChiang,China

2010 ◽  
Vol 39 ◽  
pp. 555-561 ◽  
Author(s):  
Qing Hua Luan ◽  
Yao Cheng ◽  
Zha Xin Ima
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flood Control ◽  
Back Propagation ◽  
Forecast Model ◽  
Flood Forecast ◽  
Back Propagation Algorithm ◽  
Nonlinear Method ◽  
Flood Prediction ◽  
Artificial Neural

The establishing of a precise simulation model for runoff prediction in river with several tributaries is the difficulty of flood forecast, which is also one of the difficulties in hydrologic research. Due to the theory of Artificial Neural Network, using Back Propagation algorithm, the flood forecast model for ShiLiAn hydrologic station in Minjiang River is constructed and validated in this study. Through test, the result shows that the forecast accuracy is satisfied for all check standards of flood forecast and then proves the feasibility of using nonlinear method for flood forecast. This study provides a new method and reference for flood control and water resources management in the local region.

Forecasting Crude Oil price using Artificial Neural Network model

2021 ◽  
pp. 321-326
Author(s):  
Sivaprakash J. ◽  
Manu K. S.
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Crude Oil ◽  
Back Propagation ◽  
Oil Prices ◽  
Oil Price ◽  
Back Propagation Algorithm ◽  
Crude Oil Price ◽  
Propagation Algorithm ◽  
Crude Oil Prices

In the advanced global economy, crude oil is a commodity that plays a major role in every economy. As Crude oil is highly traded commodity it is essential for the investors, analysts, economists to forecast the future spot price of the crude oil appropriately. In the last year the crude oil faced a historic fall during the pandemic and reached all time low, but will this situation last? There was analysis such as fundamental analysis, technical analysis and time series analyses which were carried out for predicting the movement of the oil prices but the accuracy in such prediction is still a question. Thus, it is necessary to identify better methods to forecast the crude oil prices. This study is an empirical study to forecast crude oil prices using the neural networks. This study consists of 13 input variables with one target variable. The data are divided in the ratio 70:30. The 70% data is used for training the network and 30% is used for testing. The feed forward and back propagation algorithm are used to predict the crude oil price. The neural network proved to be efficient in forecasting in the modern era. A simple neural network performs better than the time series models. The study found that back propagation algorithm performs better while predicting the crude oil price. Hence, ANN can be used by the investors, forecasters and for future researchers.

Application of Improved Back Propagation Neural Network for the Recognition of Composite Insulator Hydrophobicity Grade

2013 ◽  
Vol 373-375 ◽  
pp. 1155-1158
Author(s):  
Kang Yan ◽  
Zhong Yuan Zhang
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Median Filter ◽  
Back Propagation ◽  
Histogram Equalization ◽  
Back Propagation Neural Network ◽  
Threshold Method ◽  
Safe Operation ◽  
Composite Insulator ◽  
Adaptive Median Filter

The detection of hydrophobicity is an important way to evaluate the performance of composite insulator, which is helpful to the safe operation of composite insulator. In this paper, the image processing technology and Back Propagation neural network is introduced to recognize the composite insulator hydrophobicity grade. First, hydrophobic image is preprocessed by histogram equalization and adaptive median filter, then the image was segmented by Ostu threshold method, and four features associated with hydrophobicity are extracted. Finally, the improved Back Propagation neural network is adopted to recognize composite insulator hydrophobicity grade. The experimental results show that the improved Back Propagation neural network can accurately recognize the composite insulator hydrophobicity

Emotion Recognition From Speech Using Perceptual Filter and Neural Network

2020 ◽  
pp. 78-91 ◽  
Author(s):  
Revathi A. ◽  
Sasikaladevi N.
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Vector Quantization ◽  
Group Performance ◽  
Back Propagation ◽  
Critical Band ◽  
Emotion Classification ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Speaker Independent

This chapter on multi speaker independent emotion recognition encompasses the use of perceptual features with filters spaced in Equivalent rectangular bandwidth (ERB) and BARK scale and vector quantization (VQ) classifier for classifying groups and artificial neural network with back propagation algorithm for emotion classification in a group. Performance can be improved by using the large amount of data in a pertinent emotion to adequately train the system. With the limited set of data, this proposed system has provided consistently better accuracy for the perceptual feature with critical band analysis done in ERB scale.

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