Results 211 to 220 of about 2,089,547 (271)
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IEEE Transactions on Neural Networks and Learning Systems, 2020
The Levenberg–Marquardt and Newton are two algorithms that use the Hessian for the artificial neural network learning. In this article, we propose a modified Levenberg–Marquardt algorithm for the artificial neural network learning containing the training
José de Jesús Rubio
semanticscholar +1 more source
The Levenberg–Marquardt and Newton are two algorithms that use the Hessian for the artificial neural network learning. In this article, we propose a modified Levenberg–Marquardt algorithm for the artificial neural network learning containing the training
José de Jesús Rubio
semanticscholar +1 more source
2018
Artificial neural networks are perhaps the most common method amongst intelligent methods in geophysics and are becoming increasingly popular. Because they are universal approximations, these tools can approximate any continuous function with any arbitrary precision.
Alireza Hajian, Peter Styles
+8 more sources
Artificial neural networks are perhaps the most common method amongst intelligent methods in geophysics and are becoming increasingly popular. Because they are universal approximations, these tools can approximate any continuous function with any arbitrary precision.
Alireza Hajian, Peter Styles
+8 more sources
Artificial Neural Networks [PDF]
, 2008 Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods.
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Fault Detection Method based on Artificial Neural Network for 330kV Nigerian Transmission Line
International Journal of Innovative Science and Research TechnologyThis research focused on identifying various types of faults occurring on 330kV transmission lines through the use of artificial neural networks (ANN).
Alhassan Musa Oruma +5 more
semanticscholar +1 more source
IEEE Transactions on Smart Grid, 2019
Ever-changing variables in the electricity market require energy management systems (EMSs) to make optimal real-time decisions adaptively. Demand response (DR) is the latest approach being used to accelerate the efficiency and stability of power systems.
Renzhi Lu, S. Hong, Mengmeng Yu
semanticscholar +1 more source
Ever-changing variables in the electricity market require energy management systems (EMSs) to make optimal real-time decisions adaptively. Demand response (DR) is the latest approach being used to accelerate the efficiency and stability of power systems.
Renzhi Lu, S. Hong, Mengmeng Yu
semanticscholar +1 more source
2006
The primary aim of this chapter is to present an overview of the artificial neural network basics and operation, architectures, and the major algorithms used for training the neural network models. As can be seen in subsequent chapters, neural networks have made many useful contributions to solve theoretical and practical problems in finance and ...
Joarder Kamruzzaman, Ruhul A. Sarker
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The primary aim of this chapter is to present an overview of the artificial neural network basics and operation, architectures, and the major algorithms used for training the neural network models. As can be seen in subsequent chapters, neural networks have made many useful contributions to solve theoretical and practical problems in finance and ...
Joarder Kamruzzaman, Ruhul A. Sarker
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IEEE Circuits and Devices Magazine, 1988
Examines the following questions associated with artificial neural networks: why people are interested in artificial neural networks; what artificial neural networks are, from the point of view of electronic circuits, and how they work; how they can be programmed and made to solve particular problems; and whether interesting problems can actually be ...
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Examines the following questions associated with artificial neural networks: why people are interested in artificial neural networks; what artificial neural networks are, from the point of view of electronic circuits, and how they work; how they can be programmed and made to solve particular problems; and whether interesting problems can actually be ...
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[29] Artificial neural networks
1992Publisher Summary This chapter focuses on two classes of widely used artificial neural networks (ANNs), the perceptron-back-propagation and the Hopfield–Boltzmann machine models. It explores in detail the characteristics of a simple feedforward ANN model.
William T. Katz +2 more
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A review of the artificial neural network surrogate modeling in aerodynamic design
Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2019Artificial neural network surrogate modeling with its economic computational consumption and accurate generalization capabilities offers a feasible approach to aerodynamic design in the field of rapid investigation of design space and optimal solution ...
Gang Sun, Shuyue Wang
semanticscholar +1 more source
2007
Artificial neural networks are computational models of the brain. There are many types of neural networks representing the brain’s structure and operation with varying degrees of sophistication. This chapter provides an introduction to the main types of networks and presents examples of each type.
A. A. Afify +2 more
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Artificial neural networks are computational models of the brain. There are many types of neural networks representing the brain’s structure and operation with varying degrees of sophistication. This chapter provides an introduction to the main types of networks and presents examples of each type.
A. A. Afify +2 more
openaire +4 more sources