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The Lancet, 1997
Interest in artificial neural networks (ANN) has grown rapidly over the past few years. This followed a long period of low activity in the field, since Minsky and Papert [142] published their book Perceptrons with proofs showing the limitations of the one layer networks.
M, Buyse, P, Piedbois
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Interest in artificial neural networks (ANN) has grown rapidly over the past few years. This followed a long period of low activity in the field, since Minsky and Papert [142] published their book Perceptrons with proofs showing the limitations of the one layer networks.
M, Buyse, P, Piedbois
openaire +3 more sources
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
+4 more sources
2013
A traditional digital computer does many tasks very well. It's quite fast, and it does exactly what you tell it to do. Unfortunately, it can't help you when you yourself don't fully understand the problem you want to be solved. Even worse, standard algorithms don't deal well with noisy or incomplete data, yet in the real world, that's frequently the ...
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A traditional digital computer does many tasks very well. It's quite fast, and it does exactly what you tell it to do. Unfortunately, it can't help you when you yourself don't fully understand the problem you want to be solved. Even worse, standard algorithms don't deal well with noisy or incomplete data, yet in the real world, that's frequently the ...
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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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EVOLUTIONARY ARTIFICIAL NEURAL NETWORKS
International Journal of Neural Systems, 1993Evolutionary artificial neural networks (EANNs) can be considered as a combination of artificial neural networks (ANNs) and evolutionary search procedures such as genetic algorithms (GAs). This paper distinguishes among three levels of evolution in EANNs, i.e. the evolution of connection weights, architectures and learning rules. It first reviews each
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String Matching Artificial Neural Networks
International Journal of Neural Systems, 2001Three artificial neural networks (ANNs) are proposed for solving a variety of on- and off-line string matching problems. The ANN structure employed as the building block of these ANNs is derived from the harmony theory (HT) ANN, whereby the resulting string matching ANNs are characterized by fast match-mismatch decisions, low computational complexity,
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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
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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
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The paper “Artificial Neural Networks: An Overview” explains how ANNs work by mimicking the human brain. It covers their basic structure—input, hidden, and output layers—and describes types like Feed Forward, Recurrent, and Convolutional Neural Networks. The paper also shows how ANNs are used in machine learning, data security, and pattern recognition.
Ajay K B, Teja A
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Ajay K B, Teja A
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2016
The present investigation tries to achieve the objective of representation of climatic vulnerability to the hydropower plants by the adaptation of a two step approach. In the first step the Multi Criteria Decision Making was used to identify the priority value of the priority parameters.
Mrinmoy Majumder, Apu K. Saha
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The present investigation tries to achieve the objective of representation of climatic vulnerability to the hydropower plants by the adaptation of a two step approach. In the first step the Multi Criteria Decision Making was used to identify the priority value of the priority parameters.
Mrinmoy Majumder, Apu K. Saha
openaire +1 more source