Showing 1-25 of 49,592 resultsfor "Index Terms":Artificial Neural Network Method
"Index Terms":Artificial Neural Network Method
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A Mesh-less Method Based on Artificial Neural Network for Solving Poisson Equation
2018 Cross Strait Quad-Regional Radio Science and Wireless Technology Conference (CSQRWC)
Year: 2018 | Conference Paper |
Cited by: Papers (1)
A new mesh-less method based on artificial neural network is presented for solving Poisson equation. Artificial neural network functions are used as trial functions instead of traditional superposition of basis functions. The least square method is adopted to construct the error function according to the electromagnetic problems. Minimization of the error function is implemented by adjusting the n...Show More
Predicting Moroccan Real Network's Power Flow Employing the Artificial Neural Networks
2019 Third International Conference on Intelligent Computing in Data Sciences (ICDS)
Year: 2019 | Conference Paper |
Cited by: Papers (1)
In the field of the electrical network, the new global trend is to use methods of Artificial Intelligence to solve problems related to this field. In this paper, we worked on solving the Power Flow problem by predicting the values of voltage magnitudes and voltage phase angles at each bus in the network using the Artificial Neural Networks method. The results showed that the neural network method ...Show More
MW-OBS: An improved pruning method for topology design of neural networks
Tsinghua Science and Technology
Cited by: Papers (1)
Topology design of artificial neural networks (ANNs) is an important problem for large scale applications. This paper describes a new efficient pruning method, the multi-weight optimal brain surgeon (MWOBS) method, to optimize neural network topologies. The advantages and disadvantages of the OBS and unit-OBS were analyzed to develop the method. Actually, optimized topologies are difficult to get ...Show More
MW-obs: An improved pruning method for topology design of neural networks
Topology design of artificial neural networks (ANNs) is an important problem for large scale applications. This paper describes a new efficient pruning method, the multi-weight optimal brain surgeon (MWOBS) method, to optimize neural network topologies. The advantages and disadvantages of the OBS and unit-OBS were analyzed to develop the method. Actually, optimized topologies are difficult to get ...Show More
Convergence of gradient method with momentum for two-Layer feedforward neural networks
A gradient method with momentum for two-layer feedforward neural networks is considered. The learning rate is set to be a constant and the momentum factor an adaptive variable. Both the weak and strong convergence results are proved, as well as the convergence rates for the error function and for the weight. Compared to the existing convergence results, our results are more general since we do not...Show More
A New Jacobian Matrix for Optimal Learning of Single-Layer Neural Networks
This paper investigates the learning of a wide class of single-hidden-layer feedforward neural networks (SLFNs) with two sets of adjustable parameters, i.e., the nonlinear parameters in the hidden nodes and the linear output weights. The main objective is to both speed up the convergence of second-order learning algorithms such as Levenberg–Marquardt (LM), as well as to improve the network perform...Show More
Approximate Entropy-Based Epileptic EEG Detection Using Artificial Neural Networks
The electroencephalogram (EEG) signal plays an important role in the diagnosis of epilepsy. The EEG recordings of the ambulatory recording systems generate very lengthy data and the detection of the epileptic activity requires a time-consuming analysis of the entire length of the EEG data by an expert. The traditional methods of analysis being tedious, many automated diagnostic systems for epileps...Show More
Urban land suitability evaluation based on BP network
2009 17th International Conference on Geoinformatics
Year: 2009 | Conference Paper |
The artificial neural network (ANN) is a frontier theory of complex non-linearity scientific and artificial intelligence science. At present, the theory is seldom used for urban land suitability evaluation. It is a new method that combines geographic information system (GIS) and artificial neural network technology together. In order to evaluate urban land suitability properly, a new analysis inde...Show More
The inverse design of electron lens is realized by two different methods in this paper. One is damped least square method and the other is the artificial neural network method. Their merits and defects are discussed according to our calculation results in the paper. In the condition of selecting the learning samples properly, the artificial neural network has obvious advantages in the inverse desi...Show More
Optimization of instantaneous torque shape of PM motors using artificial neural networks based on FE results
IEEE Transactions on Magnetics
Cited by: Papers (15)
A method that combines artificial neural networks (ANN) and finite-elements method is introduced to estimate the instantaneous torque of two classes of permanent magnet motors. Using parallel supervised multilayer neural networks, the geometrical parameters of the motors are mapped to the developed instantaneous torque. The obtained results show a close agreement between the ANN estimated torques ...Show More
Various hardware implementations of neural networks have been studied well in recent years. We have already proposed a hardware implementation method for neural network with a Network on Chip (NoC) architecture. A mapping of a neural network on NoC should be tuned to achieve high performance whenever neural network application is changed, so that different mapping methods are needed every time and...Show More
The Rosenblatt Bayesian Algorithm Learning in a Nonstationary Environment
In this letter, we study online learning in neural networks (NNs) obtained by approximating Bayesian learning. The approach is applied to Gibbs learning with the Rosenblatt potential in a nonstationary environment. The online scheme is obtained by the minimization (maximization) of the Kullback-Leibler divergence (cross entropy) between the true posterior distribution and the parameterized one. Th...Show More
Power Flow Analysis by Numerical Techniques and Artificial Neural Networks
Meriem Fikri;Bouchra Cheddadi;Omar Sabri;Tonria Haidi;Belfqih Abdelaziz;Meriem Majdoub
2018 Renewable Energies, Power Systems & Green Inclusive Economy (REPS-GIE)
Year: 2018 | Conference Paper |
Cited by: Papers (12)
This paper discusses different Power Flow Analysis methods. The methods are either Numerical Techniques as the Newton-Raphson (NR) and Gauss-Seidel (GS) methods, or the Artificial Neural Networks (ANNs) method. To solve the power flow problem, we developed Matlab programs using both NR and GS methods. Then we established and trained an artificial neural networks. We found that even though the NR m...Show More
A Taguchi particle swarm optimization (TPSO) with a three-layer feedforward artificial neural network (ANN) is used to model and optimize the chemical composition of a steel bar. The novel contribution of a TPSO is the use of a Taguchi method mechanism to exploit better solutions in the search space through iterations, the use of the conventional non-linear PSO to increase convergence speed, and t...Show More
A neural network approach for remaining useful life prediction utilizing both failure and suspension data
2010 Proceedings - Annual Reliability and Maintainability Symposium (RAMS)
Year: 2010 | Conference Paper |
Cited by: Papers (8)
Artificial neural network (ANN) methods have shown great promise in achieving more accurate equipment remaining useful life prediction. However, most reported ANN methods only utilize condition monitoring data from failure histories, and ignore data obtained from suspension histories in which equipments are taken out of service before they fail. Suspension history condition monitoring data contain...Show More
Remaining useful life prediction using hybrid neural network and genetic algorithm approaches
Neha Kumari;Ranjan Kumar;Amiya R. Mohanty;Satyendra K. Singh;Sujit K. Mandal;Prabhat K. Mandal
2021 International Conference on Maintenance and Intelligent Asset Management (ICMIAM)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
Recently, numerous approaches have been applied for predicting the RUL of machinery based on condition information. Artificial Intelligence (AI) methods such as Long Short-Term Memory (LSTM), Feed Forward Neural Network (FFNN), Convoluted Neural Network (CNN), Recurrent Neural Network (RNN), and many more have been applied successfully in detecting the faults and predicting the RUL of machines. Bu...Show More
Constructive Approximation to Multivariate Function by Decay RBF Neural Network
It is well known that single hidden layer feedforward networks with radial basis function (RBF) kernels are universal approximators when all the parameters of the networks are obtained through all kinds of algorithms. However, as observed in most neural network implementations, tuning all the parameters of the network may cause learning complicated, poor generalization, overtraining and unstable. ...Show More
Defect Identification Using Artificial Neural Networks And Finite Element Method
2006 1ST IEEE International Conference on E-Learning in Industrial Electronics
Year: 2006 | Conference Paper |
Cited by: Papers (2)
This paper presents an approach which is based on the use of artificial neural networks and finite element analysis to solve the inverse problem of defect identification. The approach is used to identify unknown defects in metallic walls. The methodology used in this study consists in the simulation of a large number of defects in a metallic wall, using the finite element method. Both variations i...Show More
Vibration reliability analysis of turbine blade based on ANN and Monte Carlo simulation
2010 Sixth International Conference on Natural Computation
Year: 2010 | Conference Paper |
Cited by: Papers (1)
Dynamic stress of turbine blade has great influence on its reliability and fatigue life. In order to decrease the magnitude of dynamic stress, frequency modulation method is often used to avoid resonance, which implies the frequency of active force must be kept away from the inherent vibration frequency of blade. At present, many models of calculating inherent vibration frequency of blade are dete...Show More
Advances in Artificial Neural Networks for Electromagnetic Parameterized Modeling
2021 13th Global Symposium on Millimeter-Waves & Terahertz (GSMM)
Year: 2021 | Conference Paper |
Electromagnetic (EM) parameterized modeling is important for EM repetitive analysis, such as EM optimization, what if analysis, and yield optimization. An overview of advances in artificial neural networks (ANNs) for EM parameterized modeling is presented in this paper, covering forward/inverse modeling, deep neural networks, knowledge-based neural networks, neuro-transfer functions, and applicati...Show More
Application of ANN for solar radiation forecasting - case study of Oryahovo
Zahari Zarkov;Ludmil Stoyanov;Iva Draganovska;Vladimir Lazarov
2019 11th Electrical Engineering Faculty Conference (BulEF)
Year: 2019 | Conference Paper |
Cited by: Papers (2)
The forecasting of photovoltaic output power is one of the major problems for integration of large-scale solar systems for energy producing into the grid. The most challenging aspect of the solar forecasting is to predict the very short term of meteorological variables, such as solar radiance, ambient temperature, and cloud movement variations. Several forecast techniques for prediction of the sol...Show More
Magnetostatic Analysis of SRM Supported by Hybrid FEM-ANN Method
2018 International Symposium on Fundamentals of Electrical Engineering (ISFEE)
Year: 2018 | Conference Paper |
In this article the hybrid method combining the FEM with artificial neural network is depicted. The goal of the method is to obtain the magnetostatic characteristics of the switched reluctance motor. The method focuses on maximal computation time reduction and simultaneously on keeping the solution accuracy. The model of the artificial neural network is based on reduced FEM simulation. For neural ...Show More
Calculation method study on spring back of automobile ceiling
2010 2nd International Conference on Computer Engineering and Technology
Year: 2010 | Conference Paper |
Spring back is a mainly quality defect in the process of automobile ceiling stamping. Though researchers have made a lot of work on spring back by analytical methods or tests, superficial understanding or single method exists in the research of spring back prediction and control. Furthermore, Neural Network technology has applied in spring back prediction, but the method of Back Propagation Neural...Show More
Noise Immunity of UWB Positioning System on ANN
Ilya Persanov;Vadym Plakhtii;Oleksandr Pryshchenko;Oleksandr Dumin;Pylyp Fomin
The stability of work of ultrawideband positioning system with the use of impulse form classification is analyzed at presence of noise in received signal. The model of the two-dimensional system contains two bow-tie antennas excited by short Gaussian pulses. The electromagnetic problem is solved by FDTD method. The deformation of time form of received ultrashort pulses of electromagnetic field at ...Show More
Artificial Neural Network (ANN) Based Fast and Accurate Inductor Modeling and Design
This paper analyzes the potential of Artificial Neural Networks (ANNs) for the modeling and optimization of magnetic components and, specifically, inductors. After reviewing the basic properties of ANNs, several potential modeling and design workflows are presented. A hybrid method, which combines the accuracy of 3D Finite Element Method (FEM) and the low computational cost of ANNs, is selected an...Show More
Standards Dictionary Terms
- CNN
- DNN
- P
- basic symbol vector:
- block circulant matrix:
- block structured matrix:
- circulant matrix:
- compact representation:
- compressed representation of neural network:
- convolution kernel granularity and quantization:
- convolutional neural network:
- correct input vector:
- disturbance vector:
- encapsulation representation:
- high precision:
- interoperable representation:
- layer:
- list of unique offset vectors:
- low precision:
- model compression:
- model:
- multiple INT4 quantization:
- neural network representation:
- offset, offset vector:
- quantitative scale factor:
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