A Radial Base Localized Distribution for Deterministic Function Approximation Neural Networks.(Dept.E)

MEJ. Mansoura Engineering Journal ◽

10.21608/bfemu.2021.149971 ◽

2021 ◽

Vol 23 (3) ◽

pp. 1-9

Author(s):

K. El-Serafi ◽

K. Moustafa

Keyword(s):

Neural Networks ◽

Function Approximation ◽

Deterministic Function

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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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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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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 ◽

Author(s):

Shaghayegh Vahdat ◽

Mehdi Kamal ◽

Ali Afzali-Kusha ◽

Massoud Pedram

Keyword(s):

Neural Networks ◽

Function Approximation ◽

Memristive Neural Networks

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Noise-Boosted Backpropagation Learning of Feedforward Threshold Neural Networks for Function Approximation

IEEE Transactions on Instrumentation and Measurement ◽

10.1109/tim.2021.3121502 ◽

2021 ◽

Vol 70 ◽

pp. 1-12

Author(s):

Lingling Duan ◽

Fabing Duan ◽

Francois Chapeau-Blondeau ◽

Derek Abbott

Keyword(s):

Neural Networks ◽

Function Approximation ◽

Backpropagation Learning

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Generating Artificial Neural Networks for Value Function Approximation in a Domain Requiring a Shifting Strategy

Applications of Evolutionary Computation - Lecture Notes in Computer Science ◽

10.1007/978-3-642-37192-9_30 ◽

2013 ◽

pp. 294-303 ◽

Author(s):

Ransom K. Winder

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Function Approximation ◽

Value Function ◽

Value Function Approximation ◽

Shifting Strategy ◽

Artificial Neural

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Fuzzy min-max neural networks for function approximation

IEEE International Conference on Neural Networks ◽

10.1109/icnn.1993.298858 ◽

2002 ◽

Author(s):

P.K. Simpson ◽

G. Jahns

Keyword(s):

Neural Networks ◽

Function Approximation

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Design of wavelet neural networks based on symmetry fuzzy C-means for function approximation

Neural Computing and Applications ◽

10.1007/s00521-013-1350-x ◽

2013 ◽

Vol 23 (S1) ◽

pp. 247-259 ◽

Author(s):

Zarita Zainuddin ◽

Pauline Ong

Keyword(s):

Neural Networks ◽

Function Approximation ◽

Wavelet Neural Networks ◽

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Neural Networks for Function Approximation

End to End Adaptive Congestion Control in TCP/IP Networks - Automation and Control Engineering ◽

10.1201/b11870-17 ◽

2012 ◽

Keyword(s):

Neural Networks ◽

Function Approximation

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Random sampler M-estimator algorithm for robust function approximation via feed-forward neural networks

The 2011 International Joint Conference on Neural Networks ◽

10.1109/ijcnn.2011.6033636 ◽

2011 ◽

Author(s):

Moumen T. El-Melegy

Keyword(s):

Neural Networks ◽

Function Approximation ◽

Feed Forward ◽

M Estimator ◽

Feed Forward Neural Networks

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Function approximation with neural networks for obtaining an operating point sufficiently small signal stable in power systems including wind parks

2009 IEEE International Electric Machines and Drives Conference ◽

10.1109/iemdc.2009.5075302 ◽

2009 ◽

Author(s):

Carlos Gallardo ◽

Pablo Ledesma

Keyword(s):

Neural Networks ◽

Power Systems ◽

Function Approximation ◽

Operating Point ◽

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