Transform Learning Based Function Approximation for Regression and Forecasting

Advanced Analytics and Learning on Temporal Data - Lecture Notes in Computer Science ◽

10.1007/978-3-030-39098-3_2 ◽

2020 ◽

pp. 14-25

Author(s):

Kriti Kumar ◽

Angshul Majumdar ◽

M. Girish Chandra ◽

A. Anil Kumar

Keyword(s):

Function Approximation

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On the Use of Function Approximation Potential of Artificial Neural Networks for Predicting Elastic Moduli of Binary Oxide Glass Systems

Global Journal For Research Analysis ◽

10.15373/22778160/february2014/60 ◽

2012 ◽

Vol 3 (2) ◽

pp. 186-189

Author(s):

Dr.R.Sheelarani Dr.R.Sheelarani ◽

◽

Dr. K.T. Arulmozhi Dr. K.T. Arulmozhi

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Function Approximation ◽

Elastic Moduli ◽

Binary Oxide ◽

Oxide Glass ◽

Artificial Neural

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A Technique for Improving the Positioning Accuracy of Maximum-Correlation Radio Navigation Systems Through the Use of Decision Function Approximation Methods

Telecommunications and Radio Engineering ◽

10.1615/telecomradeng.v58.i9-10.150 ◽

2002 ◽

Vol 58 (9-10) ◽

pp. 9

Author(s):

Valeriy Ivanovich Antyufeev ◽

I.E. Bakulin ◽

V. N. Bykov ◽

A. M. Grichanyuk ◽

T.V. Miroshnik-Bykova

Keyword(s):

Function Approximation ◽

Decision Function ◽

Approximation Methods ◽

Navigation Systems ◽

Maximum Correlation ◽

Positioning Accuracy ◽

Radio Navigation

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A function approximation approach to anomaly detection in propulsionsystem test data

10.2514/6.1993-1776 ◽

1993 ◽

Cited By ~ 1

Author(s):

BRUCE WHITEHEAD ◽

W. HOYT

Keyword(s):

Anomaly Detection ◽

Test Data ◽

Function Approximation ◽

Approximation Approach

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Stochastic Simulation Techniques for Partition Function Approximation of Gibbs Random Field Images

10.21236/ada238611 ◽

1991 ◽

Cited By ~ 4

Author(s):

Gerasimos Potamianos ◽

John Goutsias

Keyword(s):

Partition Function ◽

Random Field ◽

Stochastic Simulation ◽

Function Approximation ◽

Gibbs Random Field ◽

Simulation Techniques

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Reconfigurable field‐programmable gate array‐based on‐chip learning neuromorphic digital implementation for nonlinear function approximation

International Journal of Circuit Theory and Applications ◽

10.1002/cta.3075 ◽

2021 ◽

Author(s):

Morteza Gholami ◽

Edris Zaman Farsa ◽

Gholamreza Karimi

Keyword(s):

Field Programmable Gate Array ◽

Function Approximation ◽

Nonlinear Function ◽

Digital Implementation ◽

Field Programmable ◽

Gate Array ◽

On Chip ◽

Nonlinear Function Approximation

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Gradient based empirical cumulative distribution function approximation for robust aerodynamic design

Aerospace Science and Technology ◽

10.1016/j.ast.2021.106630 ◽

2021 ◽

pp. 106630

Author(s):

Elisa Morales ◽

Andrea Bornaccioni ◽

Domenico Quagliarella ◽

Renato Tognaccini

Keyword(s):

Distribution Function ◽

Function Approximation ◽

Cumulative Distribution Function ◽

Cumulative Distribution ◽

Aerodynamic Design ◽

Empirical Cumulative Distribution Function ◽

Gradient Based

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Optimal function approximation with ReLU neural networks

Neurocomputing ◽

10.1016/j.neucom.2021.01.007 ◽

2021 ◽

Vol 435 ◽

pp. 216-227

Author(s):

Bo Liu ◽

Yi Liang

Keyword(s):

Neural Networks ◽

Function Approximation ◽

Optimal Function

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Thermal analysis of a moving fin using the radial basis function approximation

Heat Transfer ◽

10.1002/htj.22242 ◽

2021 ◽

Author(s):

Maryam Fallah Najafabadi ◽

Hossein Talebi Rostami ◽

Khashayar Hosseinzadeh ◽

Davood Domiri Ganji

Keyword(s):

Thermal Analysis ◽

Radial Basis Function ◽

Basis Function ◽

Function Approximation ◽

Radial Basis ◽

Radial Basis Function Approximation

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Concentration bounds for temporal difference learning with linear function approximation: the case of batch data and uniform sampling

Machine Learning ◽

10.1007/s10994-020-05912-5 ◽

2021 ◽

Author(s):

L. A. Prashanth ◽

Nathaniel Korda ◽

Rémi Munos

Keyword(s):

Linear Function ◽

Function Approximation ◽

Temporal Difference ◽

Temporal Difference Learning ◽

Uniform Sampling ◽

Linear Function Approximation ◽

Concentration Bounds ◽

Batch Data

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Interstice: Inverter-Based Memristive Neural Networks Discretization for Function Approximation Applications

IEEE Transactions on Very Large Scale Integration (VLSI) Systems ◽

10.1109/tvlsi.2020.2991795 ◽

2020 ◽

Vol 28 (7) ◽

pp. 1578-1588 ◽

Cited By ~ 1

Author(s):

Shaghayegh Vahdat ◽

Mehdi Kamal ◽

Ali Afzali-Kusha ◽

Massoud Pedram

Keyword(s):

Neural Networks ◽

Function Approximation ◽

Memristive Neural Networks

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