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The Need for Recurrent Learning Neural Network and Combine Pareto Differential Algorithm for Multi-Objective Optimization of Real Time Reservoir Operations
2020
Journal of Soft Computing in Civil Engineering
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This review study, based on systematic literature analysis, presents the suitability of Recurrent Learning Neural Network (RLNN) and Combine Pareto Multi-objective Differential Evolution (CPMDE) algorithms ...
for real time data analytics and multi-objective optimization of reservoir operations, respectively. ...
Multilayer feedforward [4, 20, [32] [33] [34] [35] [36] [37] [38] [39] , backpropagation [38] , recurrent learning neural networks [9, 10] and single layer feedforward [19, 40] are noticeable ANN ...
doi:10.22115/scce.2020.226578.1204
doaj:40d7cd8a08ce440fad9ebc7a1c1b2b60
fatcat:2kumgxh46vfmhoa4mkmzqnpgd4
HAVAYOLU FİRMALARININ ÇOK KRİTERLİ OY DEĞERLERİ İÇİN NİTELİK ANALİZİ
2020
Mühendislik Bilimleri ve Tasarım Dergisi
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Keywords Abstract Multi Criteria Decision Making, Artificial Neural Network, Multi-layer Perceptron, Promethee II, Airline Companies. ...
It does so using the a multicriteria decision making technique (Promethee II), and the criteria weight values required for the Promethee II method are obtained from a Multi-Layer Perceptron (MLP), an artificial ...
However, this decision may be based on just a single criterion or multi criteria. ...
doi:10.21923/jesd.459275
fatcat:o4yzeu2a4nfj3gwn624s4apnkm
Application of Neural Networks for classification of Patau, Edwards, Down, Turner and Klinefelter Syndrome based on first trimester maternal serum screening data, ultrasonographic findings and patient demographics
2018
BMC Medical Genomics
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Results: The architectures of linear feedforward and feedback neural networks were investigated for various training data distributions and number of neurons in hidden layer. ...
Feedback neural network architecture out performed feedforward neural network architecture in predictive ability for all five aneuploidy prenatal syndrome classes. ...
Acknowledgements The authors would like to thank the editor and reviewers for the thoughtful comments and constructive suggestions, which greatly helped us improve the quality of this manuscript. ...
doi:10.1186/s12920-018-0333-2
pmid:29439729
pmcid:PMC5812210
fatcat:e7ehspskf5efdmcq2qbvihfk4e
$$F_{0}$$ F 0 Modeling Using DNN for Arabic Parametric Speech Synthesis
[chapter]
2019
Msphere
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Deep neural networks (DNN) are gaining increasing interest in speech processing applications, especially in text-to-speech synthesis. ...
In this paper, we aim to model F0 for Arabic speech synthesis with feedforward and recurrent DNN, and using specific characteristic features for Arabic like vowel quantity and gemination, in order to improve ...
Table 1 . 1 DNN architectures selected based on results on development set for voiced/unvoiced decision classification and for F0 prediction. ...
doi:10.1007/978-3-030-16841-4_20
fatcat:vz3w75o66bc5lfvihhojwh5ygq
A Model for Stock Market Value Forecasting using Ensemble Artificial Neural Network
2021
Journal of Advances in Computing Communications and Information Technology
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The output of the proposed predictive model was compared with four traditional neural network multilayer perceptron algorithms, and outperformed the traditional neural network multilayer perceptron algorithms ...
Artificial Neural Network (ANN) is a model used in capturing linear and non-linear relationship of input and output data. ...
The results obtained revealed that the model performed best for the selected criteria. ...
doi:10.37121/jaccit.v2.162
fatcat:4eecmirhdverlar52slezcfpjy
Stock Price Forecasting of E-Commerce Company Using Feedforward Backpropagation Neural Network
2019
International Journal of Advanced Trends in Computer Science and Engineering
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In this study, stock price predictions investigated by using Matlab version R2018b software using the Feedforward Backpropagation Neural Network method. ...
The results of the study proved that using artificial neural networks can predict the price of a stock whose price is very close to the actual price with a very small mean square error (MSE) value and ...
For model performance criteria, statistical criteria are adopted here to help select the desired optimal network model. ...
doi:10.30534/ijatcse/2019/4081.52019
fatcat:2bihlelg2negfoof7h5dkdmwk4
Pruning algorithms of neural networks — a comparative study
2013
Open Computer Science
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AbstractThe neural network with optimal architecture speeds up the learning process and generalizes the problem well for further knowledge extraction. ...
As a result researchers have developed various techniques for pruning the neural networks. ...
A typical feedforward neural network contains an input layer, one or more hidden layers and an output layer. ...
doi:10.2478/s13537-013-0109-x
fatcat:sqa4fx2v65dfnbdw6pyzo5tqdq
Simulation and Forecasting Complex Economic Time Series Using Neural Network Models
2005
Journal of Intelligent Systems
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We describe in this paper the application of several neural network architectures to the problem of simulating and predicting the dynamic behavior of complex economic time series. ...
For this reason, we have chosen a neural network approach to simulate and predict the evolution of these prices in the United States market. ...
ACKNOWLEDGMENTS The authors would like to thank the research committee of SAGARPA-CONACYT for the financial support given to this project. ...
doi:10.1515/jisys.2005.14.2-3.193
fatcat:5a45baykevg2vhtz6fuxffvlwq
Multi-objective Evolutionary Neural Network to Predict Graduation Success at the United States Military Academy
2018
Procedia Computer Science
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This paper presents an evolutionary neural network approach to classify student graduation status based upon selected academic, demographic, and other indicators. ...
Abstract This paper presents an evolutionary neural network approach to classify student graduation status based upon selected academic, demographic, and other indicators. ...
Previous work on this same data set utilizing multi-layer feed forward neural network, with manual parameter sweep, required well over 50 hidden neurons and exhaustive search of the neural network architecture ...
doi:10.1016/j.procs.2018.10.329
fatcat:dqrfmh4eobehpfhhthhrx3la7a
Artificial neural network applications in the calibration of spark-ignition engines: An overview
2016
Engineering Science and Technology, an International Journal
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The potential use of trained neural networks in combination with Design of Experiments (DoE) methods for engine calibration has been a subject of research activities in recent times. ...
The demerits of neural networks, future possibilities and alternatives were also discussed. ...
financial support for the study. ...
doi:10.1016/j.jestch.2016.03.003
fatcat:tb56t3lccral3ndqylx4afjktm
Deep neural networks: a new framework for modelling biological vision and brain information processing
[article]
2015
bioRxiv
pre-print
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Convolutional feedforward networks, which now dominate computer vision, take further inspiration from the architecture of the primate visual hierarchy. ...
Artificial neural networks are inspired by the brain and their computations could be implemented in biological neurons. ...
A multi-layer network of linear units is equivalent to single-layer network whose weights matrix W' is the product of the weights matrices W i of the multi-layer network. ...
doi:10.1101/029876
fatcat:lxuwpdhzrvhpdmtyzg33ogwncy
The Neural Networks with an Incremental Learning Algorithm Approach for Mass Classification in Breast Cancer
2016
International Journal of Biomedical Data Mining
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This paper uses the neural networks with an incremental learning algorithm as a tool to classify a mass in the breast (benign and malignant) using selection of the most relevant risk factors and decision ...
making of the breast cancer diagnosis To test the proposed algorithm we used the Wisconsin Breast Cancer Database (WBCD). ...
network made up of a single neuron on its hidden layer. ...
doi:10.4172/2090-4924.1000118
fatcat:xsk53v3pyfcn3p722awfr54u3i
Neural Interaction Transparency (NIT): Disentangling Learned Interactions for Improved Interpretability
2018
Neural Information Processing Systems
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Neural networks are known to model statistical interactions, but they entangle the interactions at intermediate hidden layers for shared representation learning. ...
We show that disentangling interactions reduces a feedforward neural network to a generalized additive model with interactions, which can lead to transparent models that perform comparably to the state-of-theart ...
Acknowledgments We thank Umang Gupta and anonymous reviewers for their generous feedback. ...
dblp:conf/nips/TsangLPML18
fatcat:plmilk4ysrbynnmavupi642xuu
Co-evolutionary multi-task learning for dynamic time series prediction
[article]
2018
arXiv
pre-print
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It enables neural networks to retain modularity during training for making a decision in situations even when certain inputs are missing. ...
In this paper, we propose a co-evolutionary multi-task learning method that provides a synergy between multi-task learning and co-evolutionary algorithms to address dynamic time series prediction. ...
Each neural network architecture was tested with different numbers of hidden neurons. ...
arXiv:1703.01887v2
fatcat:g4rpkmhcmbckbokt5asruh5lo4
Artificial Neural Networks in the Prediction and Assessment for Water Quality: A Review
2019
Journal of Physics, Conference Series
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Therefore, this paper is a literature review aimed at analysing and comparing the characteristics and applications of existing artificial neural network models. ...
In order to control the water quality environment more effectively and intelligently, artificial neural network (ANN) and the hybrid models that contain it are applied to accurately and intelligently predict ...
RBFNN is a single hidden layer feedforward network based on function approximation theory [4] . ...
doi:10.1088/1742-6596/1237/4/042051
fatcat:3wv35kjxbvg6jaqcy3lnpak57m
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