Discussion: General Regression Neural Networks for Driven Piles in Cohesionless Soils

General Regression Neural Networks for Driven Piles in Cohesionless Soils

Journal of Geotechnical and Geoenvironmental Engineering ◽

10.1061/(asce)1090-0241(1998)124:12(1177) ◽

1998 ◽

Vol 124 (12) ◽

pp. 1177-1185 ◽

Cited By ~ 112

Author(s):

M. A. Abu Kiefa

Keyword(s):

Neural Networks ◽

Driven Piles ◽

Cohesionless Soils ◽

04/01537 Subsystem level fault diagnosis of a building's air-handling unit using general regression neural networks

Fuel and Energy Abstracts ◽

10.1016/s0140-6701(04)90363-3 ◽

2004 ◽

Vol 45 (3) ◽

pp. 209

Keyword(s):

Neural Networks ◽

Fault Diagnosis ◽

Air Handling Unit ◽

Subsystem Level ◽

Data preprocessing using hybrid general regression neural networks and particle swarm optimization for remote terminal units

International Journal of Control Automation and Systems ◽

10.1007/s12555-012-0220-0 ◽

2012 ◽

Vol 10 (2) ◽

pp. 407-414 ◽

Cited By ~ 5

Author(s):

Wen-Hui Chen ◽

Jun-Horng Chen ◽

Shih-Chun Shao

Keyword(s):

Neural Networks ◽

Particle Swarm Optimization ◽

Particle Swarm ◽

Data Preprocessing ◽

Swarm Optimization ◽

Remote Terminal ◽

Terminal Units ◽

Remote Terminal Units ◽

General Regression Neural Networks as rotor fault detectors of the induction motor

2010 IEEE International Conference on Industrial Technology ◽

10.1109/icit.2010.5472618 ◽

2010 ◽

Cited By ~ 3

Author(s):

Marcin Kaminski ◽

Czeslaw T. Kowalski ◽

Teresa Orlowska-Kowalska

Keyword(s):

Neural Networks ◽

Induction Motor ◽

General Regression ◽

Fault Detectors

Object oriented software quality prediction using general regression neural networks

ACM SIGSOFT Software Engineering Notes ◽

10.1145/1022494.1022515 ◽

2004 ◽

Vol 29 (5) ◽

pp. 1-6 ◽

Cited By ~ 19

Author(s):

S. Kanmani ◽

V. Rhymend Uthariaraj ◽

V. Sankaranarayanan ◽

P. Thambidurai

Keyword(s):

Neural Networks ◽

Software Quality ◽

Object Oriented ◽

Quality Prediction ◽

General Regression ◽

Software Quality Prediction

Evolving General Regression Neural Networks for Learning from Noisy Datasets

2019 IEEE Symposium Series on Computational Intelligence (SSCI) ◽

10.1109/ssci44817.2019.9003073 ◽

2019 ◽

Author(s):

Ahmad Jobran Al-Mahasneh ◽

Sreenatha G Anavatti ◽

Matthew A Garratt

Keyword(s):

Neural Networks ◽

Gap-Based Estimation: Choosing the Smoothing Parameters for Probabilistic and General Regression Neural Networks

The 2006 IEEE International Joint Conference on Neural Network Proceedings ◽

10.1109/ijcnn.2006.1716338 ◽

2006 ◽

Cited By ~ 2

Author(s):

M. Zhong ◽

D. Coggeshall ◽

E. Ghaneie ◽

T. Pope ◽

M. Rivera ◽

...

Keyword(s):

Neural Networks ◽

Smoothing Parameters ◽

Tree-Structure Ensemble General Regression Neural Networks applied to predict the molten steel temperature in Ladle Furnace

Advanced Engineering Informatics ◽

10.1016/j.aei.2016.05.001 ◽

2016 ◽

Vol 30 (3) ◽

pp. 368-375 ◽

Cited By ~ 20

Author(s):

Xiaojun Wang ◽

Mingshuang You ◽

Zhizhong Mao ◽

Ping Yuan

Keyword(s):

Neural Networks ◽

Molten Steel ◽

Tree Structure ◽

Ladle Furnace ◽

Steel Temperature ◽

Molten Steel Temperature ◽

General Regression ◽

Structure Ensemble

Evolving General Regression Neural Networks for Tsunami Detection and Response

2006 IEEE International Conference on Evolutionary Computation ◽

10.1109/cec.2006.1688613 ◽

2006 ◽

Cited By ~ 3

Author(s):

K. Casey ◽

A. Lim ◽

G. Dozier

Keyword(s):

Neural Networks ◽

Tsunami Detection ◽

2013 10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) ◽

Nearest neighbor prediction of pareto dominance using general regression neural networks

10.1109/fskd.2013.6816237 ◽

2013 ◽

Keyword(s):

Neural Networks ◽

Nearest Neighbor ◽

Pareto Dominance ◽