Deep Neural Trading: Comparative Study with Feed Forward, Recurrent and Autoencoder Networks

Communications in Computer and Information Science - Data Management Technologies and Applications ◽

10.1007/978-3-030-26636-3_9 ◽

2019 ◽

pp. 189-209

Author(s):

Gianluca Moro ◽

Roberto Pasolini ◽

Giacomo Domeniconi ◽

Vittorio Ghini

Keyword(s):

Comparative Study ◽

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A Comparative Study of Feed Forward Hybrid Neuro-Computing Framework with Multilayer Perceptron Model for Prediction of Breast Cancer

Lecture Notes in Electrical Engineering - ICCCE 2020 ◽

10.1007/978-981-15-7961-5_132 ◽

2020 ◽

pp. 1455-1463

Author(s):

M. R. Narasingarao ◽

V. Satya Aruna

Keyword(s):

Breast Cancer ◽

Comparative Study ◽

Multilayer Perceptron ◽

Feed Forward ◽

Computing Framework

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Analysis and Comparative Study of Source Separation Performances in Feed-Forward and Feed-Back BSSs Based on Propagation Delays in Convolutive Mixture

Lecture Notes in Computer Science - Artificial Neural Networks – ICANN 2007 ◽

10.1007/978-3-540-74690-4_18 ◽

2007 ◽

pp. 169-179

Author(s):

Akihide Horita ◽

Kenji Nakayama ◽

Akihiro Hirano

Keyword(s):

Comparative Study ◽

Source Separation ◽

Feed Forward ◽

Propagation Delays ◽

Convolutive Mixture ◽

Separation Performances

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Active damping control with a feed forward loop for single-phase UPS inverter system: A comparative study

2015 International Conference on Energy Economics and Environment (ICEEE) ◽

10.1109/energyeconomics.2015.7235065 ◽

2015 ◽

Author(s):

Santosh Kumar Singh ◽

S. Ghatak Choudhuri

Keyword(s):

Comparative Study ◽

Single Phase ◽

Active Damping ◽

Feed Forward ◽

Feed Forward Loop ◽

Damping Control ◽

System A ◽

Active Damping Control

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A comparative study of the feed forward back propagation (FFBP) and layer recurrent (LR) neural network model for forecasting ground level ozone concentration

Air Quality Atmosphere & Health ◽

10.1007/s11869-016-0417-0 ◽

2016 ◽

Vol 10 (2) ◽

pp. 213-223 ◽

Author(s):

A. K. Gorai ◽

Gargi Mitra

Keyword(s):

Neural Network ◽

Comparative Study ◽

Network Model ◽

Neural Network Model ◽

Ozone Concentration ◽

Back Propagation ◽

Ground Level ◽

Feed Forward ◽

Ground Level Ozone ◽

Feed Forward Back Propagation

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Comparative Study of Neural Networks Techniques in the Context of Cooperative Observations

10.5753/eniac.2019.9315 ◽

2019 ◽

Author(s):

Leonardo Costa ◽

Matheus Araújo ◽

Thayanne Silva ◽

Raimundo Junior ◽

João Andrade ◽

...

Keyword(s):

Neural Networks ◽

Comparative Study ◽

No problema de Observação Cooperativa de Alvos (CTO), um grupo de observadores em movimento deve monitorar um grupo de alvos em movimento para maximizar o número médio de alvos observados. A maioria das abordagens computacionais do CTO considera que os observadores são agentes racionais e que os alvos são apenas agentes ingênuos. Este trabalho incorpora um modelo de comportamento dos observadores no sistema de tomada de decisões dos alvos, considerando quatro modelos básicos de redes neurais treinadas, para melhorar seu desempenho. Os resultados mostraram que o desempenho da equipe-alvo aumentou quando eles foram modelados como agentes racionais, principalmente quando o modelo incorpora modelos básicos de redes neurais recorrentes em comparação com as abordagens clássicas de feed-forward.

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Comparative Study Of Deep Feed Forward Neural Network Application For Seismic Reservoir Characterization

First EAGE/PESGB Workshop Machine Learning ◽

10.3997/2214-4609.201803009 ◽

2018 ◽

Author(s):

T. Colwell ◽

Ø. Kjøsnes

Keyword(s):

Neural Network ◽

Comparative Study ◽

Reservoir Characterization ◽

Feed Forward Neural Network ◽

Feed Forward ◽

Seismic Reservoir ◽

Network Application

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Development of a modular system for estimating attenuation coefficient of gamma radiation: comparative study of different learning algorithms of cascade feed-forward neural network

Journal of Instrumentation ◽

10.1088/1748-0221/14/10/p10010 ◽

2019 ◽

Vol 14 (10) ◽

pp. P10010-P10010 ◽

Author(s):

Khalil Moshkbar-Bakhshayesh

Keyword(s):

Neural Network ◽

Comparative Study ◽

Gamma Radiation ◽

Attenuation Coefficient ◽

Learning Algorithms ◽

Modular System ◽

Feed Forward Neural Network ◽

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Optimizing Feed-Forward Neural Network Topology by Multi-objective Evolutionary Algorithms: A Comparative Study on Biomedical Datasets

Communications in Computer and Information Science - Advances in Artificial Life, Evolutionary Computation and Systems Chemistry ◽

10.1007/978-3-319-32695-5_5 ◽

2016 ◽

pp. 53-64 ◽

Author(s):

Vitoantonio Bevilacqua ◽

Fabio Cassano ◽

Ernesto Mininno ◽

Giovanni Iacca

Keyword(s):

Neural Network ◽

Evolutionary Algorithms ◽

Comparative Study ◽

Network Topology ◽

Feed Forward Neural Network ◽

Feed Forward ◽

Multi Objective

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Margin maximization with feed-forward neural networks: a comparative study with SVM and AdaBoost

Neurocomputing ◽

10.1016/j.neucom.2003.10.011 ◽

2004 ◽

Vol 57 ◽

pp. 313-344 ◽

Author(s):

Enrique Romero ◽

Lluı́s Màrquez ◽

Xavier Carreras

Keyword(s):

Neural Networks ◽

Comparative Study ◽

Feed Forward ◽

Feed Forward Neural Networks ◽

Margin Maximization

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Comparative study of logistic map series prediction using feed-forward, partially recurrent and general regression networks

Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02. ◽

10.1109/iconip.2002.1201917 ◽

2002 ◽

Author(s):

R. Mikolajczak ◽

J. Mandziuk

Keyword(s):

Comparative Study ◽

Logistic Map ◽

Feed Forward ◽

General Regression

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