A comparative study of neural networks for input resistance computation of electrically thin and thick rectangular microstrip antennas

Artificial neural networks for calculating the input resistance of circular microstrip antennas

Microwave and Optical Technology Letters ◽

10.1002/mop.10838 ◽

2003 ◽

Vol 37 (2) ◽

pp. 107-111 ◽

Cited By ~ 18

Author(s):

Kerim Guney ◽

Nurcan Sar?kaya

Keyword(s):

Neural Networks ◽

Input Resistance ◽

Microstrip Antennas ◽

3rd International Conference on Advances in Power System Control, Operation and Management (APSCOM 95) ◽

Comparative study for load forecasting using artificial neural networks “ANN” and a statistical package

10.1049/cp:19951275 ◽

1995 ◽

Author(s):

M.M. Elkateb

Keyword(s):

Neural Networks ◽

Comparative Study ◽

Load Forecasting ◽

Artificial Neural Networks for (Automotive) Embedded Control - A Comparative Study

2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC) ◽

10.1109/ccwc51732.2021.9376085 ◽

2021 ◽

Author(s):

Sven Dominka ◽

Henrik Smith ◽

Benjamin Hartmann

Keyword(s):

Neural Networks ◽

Comparative Study ◽

Embedded Control ◽

A Comparative Study of 1D-Convolutional Neural Networks with Modified Possibilistic c-Mean Algorithm for Mapping Transplanted Paddy Fields Using Temporal Data

Journal of the Indian Society of Remote Sensing ◽

10.1007/s12524-020-01303-4 ◽

2021 ◽

Author(s):

Anuvi Rawat ◽

Anil Kumar ◽

Priyadarshi Upadhyay ◽

Shashi Kumar

Keyword(s):

Neural Networks ◽

Comparative Study ◽

Convolutional Neural Networks ◽

Paddy Fields ◽

Temporal Data

A Comparative Study of Monopole and Microstrip Antennas for On-body Propagations Using a Dynamic Body Phantom Model

2020 IEEE Texas Symposium on Wireless and Microwave Circuits and Systems (WMCS) ◽

10.1109/wmcs49442.2020.9172266 ◽

2020 ◽

Author(s):

George Lee ◽

Brian Garner ◽

Yang Li

Keyword(s):

Comparative Study ◽

Microstrip Antennas ◽

Phantom Model

Sentiment analysis with deep neural networks: comparative study and performance assessment

Artificial Intelligence Review ◽

10.1007/s10462-020-09845-2 ◽

2020 ◽

Vol 53 (8) ◽

pp. 6155-6195

Author(s):

Ramesh Wadawadagi ◽

Veerappa Pagi

Keyword(s):

Neural Networks ◽

Comparative Study ◽

Performance Assessment ◽

Sentiment Analysis ◽

Deep Neural Networks ◽

And Performance

Point-Wise Confidence Interval Estimation by Neural Networks: A Comparative Study based on Automotive Engine Calibration

Neural Computing and Applications ◽

10.1007/s005210050009 ◽

1999 ◽

Vol 8 (1) ◽

pp. 77-85 ◽

Cited By ~ 22

Author(s):

D. Lowe ◽

C. Zapart

Keyword(s):

Neural Networks ◽

Confidence Interval ◽

Comparative Study ◽

Interval Estimation ◽

Automotive Engine ◽

Engine Calibration ◽

Confidence Interval Estimation

A comparative study of expansion functions for evolutionary hybrid functional link artificial neural networks for data mining and classification

2013 International Conference on Computer Applications Technology (ICCAT) ◽

10.1109/iccat.2013.6521977 ◽

2013 ◽

Cited By ~ 11

Author(s):

F. Mili ◽

M. Hamdi

Keyword(s):

Data Mining ◽

Neural Networks ◽

Comparative Study ◽

Hybrid Functional ◽

Functional Link ◽

Reconstruction of Storm‐Time Total Electron Content Using Ionospheric Tomography and Artificial Neural Networks: A Comparative Study Over the African Region

Radio Science ◽

10.1029/2017rs006499 ◽

2018 ◽

Vol 53 (11) ◽

pp. 1328-1345 ◽

Cited By ~ 4

Author(s):

Jean Claude Uwamahoro ◽

Nigussie M. Giday ◽

John Bosco Habarulema ◽

Zama T. Katamzi‐Joseph ◽

Gopi Krishna Seemala

Keyword(s):

Neural Networks ◽

Comparative Study ◽

Total Electron Content ◽

Electron Content ◽

African Region ◽

Total Electron ◽

Ionospheric Tomography ◽

Comparative study on the performance of neural networks for prediction in assisting visual servoing

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-171475 ◽

2019 ◽

Vol 36 (1) ◽

pp. 675-688 ◽

Cited By ~ 1

Author(s):

A.T. Vijayan ◽

S. Ashok

Keyword(s):

Neural Networks ◽

Comparative Study ◽

Visual Servoing