Results 221 to 230 of about 2,089,547 (271)
Some of the next articles are maybe not open access.
Unsupervised Learning Based On Artificial Neural Network: A Review
IEEE International Conference on Cyborg and Bionic Systems, 2018Artificial neural networks (ANN) have been applied effectively in numerous fields for the aim of prediction, knowledge discovery, classification, time series analysis, modeling, etc.
Happiness Ugochi Dike +3 more
semanticscholar +1 more source
Bitcoin technical trading with artificial neural network
Physica A: Statistical Mechanics and its Applications, 2018This paper explores Bitcoin intraday technical trading based on artificial neural networks for the return prediction. In particular, our deep learning method successfully discovers trading signals through a seven layered neural network structure for ...
M. Nakano +2 more
semanticscholar +1 more source
Artificial Neural Networks: An Overview
Network: Computation in Neural Systems, 2008Neural networks have been a much publicized topic of research in recent years and are now beginning to be used in a wide range of subject areas.
openaire +3 more sources
2019
This chapter will cover the basics of artificial neural networks (ANNs) which are also called multilayer perceptrons. Neural networks are networks of interconnected artificial neurons. Their structure is heavily inspired by the brain’s neuron network.
Gopinath Rebala +2 more
+7 more sources
This chapter will cover the basics of artificial neural networks (ANNs) which are also called multilayer perceptrons. Neural networks are networks of interconnected artificial neurons. Their structure is heavily inspired by the brain’s neuron network.
Gopinath Rebala +2 more
+7 more sources
2013
In models of large networks of neurons, the behavior of individual neurons is treated much simpler than in the Hodgkin-Huxley theory presented in Chapter 1: activity is usually represented by a binary variable (1 = firing; 0 = silent) and time is modeled by a discrete sequence of time steps running in synchrony for all neurons in the net.
Hasan Erdal +3 more
+7 more sources
In models of large networks of neurons, the behavior of individual neurons is treated much simpler than in the Hodgkin-Huxley theory presented in Chapter 1: activity is usually represented by a binary variable (1 = firing; 0 = silent) and time is modeled by a discrete sequence of time steps running in synchrony for all neurons in the net.
Hasan Erdal +3 more
+7 more sources
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.
Apu Kumar Saha, Mrinmoy Majumder
openaire +2 more sources
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.
Apu Kumar Saha, Mrinmoy Majumder
openaire +2 more sources
Artificial Neural Networks [PDF]
, 2011 Artificial Neural Networks (ANN) are inspired by the way biological neural system works, such as the brain process information. The information processing system is composed of a large number of highly interconnected processing elements (neurons) working together to solve specific problems. ANNs, just like people, learn by example.
Crina Grosan, Ajith Abraham
openaire +1 more source
2010
Artificial Neural Networks (ANN) are an inspiration from the human brain. These systems contain a large number of neurons that work in a parallel architecture. Each neuron takes its input directly from system or from other neurons. The information is processed and given to the other neurons.
Rahul Kala, Ritu Tiwari, Anupam Shukla
openaire +2 more sources
Artificial Neural Networks (ANN) are an inspiration from the human brain. These systems contain a large number of neurons that work in a parallel architecture. Each neuron takes its input directly from system or from other neurons. The information is processed and given to the other neurons.
Rahul Kala, Ritu Tiwari, Anupam Shukla
openaire +2 more sources
2001
As has been seen previously, fuzzy logic arose from the attempt to emulate the imprecise and incomplete way of treating information that is typical of the human brain. Another attempt in this direction is represented by neural networks, which were born as neural structure models of the brain but are currently used as calculation paradigms for ...
Giuseppe Nunnari +5 more
openaire +2 more sources
As has been seen previously, fuzzy logic arose from the attempt to emulate the imprecise and incomplete way of treating information that is typical of the human brain. Another attempt in this direction is represented by neural networks, which were born as neural structure models of the brain but are currently used as calculation paradigms for ...
Giuseppe Nunnari +5 more
openaire +2 more sources
2020
During the last years much effort was expended to combine (quantum) physics with machine learning methods. This has led to many useful and interesting results. One of those was an ansatz to parametrize quantum spin-1/2 systems with a generative artificial neural network, specifically the restricted Boltzmann machine.
openaire +4 more sources
During the last years much effort was expended to combine (quantum) physics with machine learning methods. This has led to many useful and interesting results. One of those was an ansatz to parametrize quantum spin-1/2 systems with a generative artificial neural network, specifically the restricted Boltzmann machine.
openaire +4 more sources