Prediction of the stress relaxation property of diene rubber composites by artificial neural network approaches

Stress Relaxation ◽

Relaxation Property ◽

Rubber Composites ◽

Artificial Neural ◽

Network Approaches ◽

Diene Rubber

An artificial neural network was established to predict the stress relaxation property of diene rubber composites during ozone aging.

Prediction of the fatigue life of natural rubber composites by artificial neural network approaches

Materials & Design (1980-2015) ◽

10.1016/j.matdes.2013.12.044 ◽

2014 ◽

Vol 57 ◽

pp. 180-185 ◽

Cited By ~ 27

Author(s):

Ke-Lu Xiang ◽

Pu-Yu Xiang ◽

You-Ping Wu

Keyword(s):

Neural Network ◽

Fatigue Life ◽

Natural Rubber ◽

Rubber Composites ◽

Natural Rubber Composites ◽

Artificial Neural ◽

Forest Land Cover Classification Using Statistical and Artificial Neural Network Approaches Applied to IRS LISS ‐ III Sensor

Geocarto International ◽

10.1080/10106040108542192 ◽

2001 ◽

Vol 16 (2) ◽

pp. 39-44 ◽

Cited By ~ 6

Author(s):

Ramanathan Sugumaran

Keyword(s):

Neural Network ◽

Land Cover ◽

Land Cover Classification ◽

Forest Land ◽

Liss Iii ◽

Artificial Neural ◽

Forest Land Cover ◽

Predictive modelling of coniferous forest age using statistical and artificial neural network approaches applied to remote sensor data

International Journal of Remote Sensing ◽

10.1080/014311699211804 ◽

1999 ◽

Vol 20 (14) ◽

pp. 2805-2822 ◽

Cited By ~ 85

Author(s):

J. R. JENSEN, FANG QIU, MINHE JI

Keyword(s):

Neural Network ◽

Coniferous Forest ◽

Predictive Modelling ◽

Sensor Data ◽

Forest Age ◽

Remote Sensor ◽

Artificial Neural ◽

Experimental and Artificial Neural Network Approaches to Predict the Effect of PVA (Poly Vinyl Acetate) on the Rheological Properties of Water and Crude Oil in EOR Processes

10.2118/140680-ms ◽

2010 ◽

Cited By ~ 1

Author(s):

A. Rezaian ◽

A. Kordestany ◽

M. Haghighat Sefat

Keyword(s):

Neural Network ◽

Crude Oil ◽

Rheological Properties ◽

Vinyl Acetate ◽

Artificial Neural ◽

Eor Processes ◽

2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society ◽

Complimentary artificial neural network approaches for prediction of events in the neonatal intensive care unit

10.1109/iembs.2008.4650239 ◽

2008 ◽

Cited By ~ 4

Author(s):

Daphne Townsend ◽

Monique Frize

Keyword(s):

Neural Network ◽

Intensive Care Unit ◽

Intensive Care ◽

Neonatal Intensive Care Unit ◽

Neonatal Intensive Care ◽

Artificial Neural ◽

Modeling of effluent COD in UAF reactor treating cyanide containing wastewater using artificial neural network approaches

Advances in Engineering Software ◽

10.1016/j.advengsoft.2010.04.002 ◽

2010 ◽

Vol 41 (7-8) ◽

pp. 1005-1010 ◽

Cited By ~ 11

Author(s):

Turan Yilmaz ◽

Galip Seckin ◽

Ahmet Yuceer

Keyword(s):

Neural Network ◽

Artificial Neural ◽

COMPARISON OF PROCESS-BASED AND ARTIFICIAL NEURAL NETWORK APPROACHES FOR STREAMFLOW MODELING IN AN AGRICULTURAL WATERSHED

JAWRA Journal of the American Water Resources Association ◽

10.1111/j.1752-1688.2006.tb04475.x ◽

2006 ◽

Vol 42 (3) ◽

pp. 545-563 ◽

Cited By ~ 51

Author(s):

Puneet Srivastava ◽

James N. McNair ◽

Thomas E. Johnson

Keyword(s):

Neural Network ◽

Agricultural Watershed ◽

Artificial Neural ◽

Network Approaches ◽

Streamflow Modeling

Application of artificial neural network in prediction of abrasion of rubber composites

Materials & Design (1980-2015) ◽

10.1016/j.matdes.2013.01.047 ◽

2013 ◽

Vol 49 ◽

pp. 802-807 ◽

Cited By ~ 10

Author(s):

Bin Wang ◽

Jian Hua Ma ◽

You Ping Wu

Keyword(s):

Neural Network ◽

Rubber Composites ◽

Artificial Neural

Rapid activity prediction of HIV-1 integrase inhibitors: harnessing docking energetic components for empirical scoring by chemometric and artificial neural network approaches

Journal of Computer-Aided Molecular Design ◽

10.1007/s10822-016-9917-0 ◽

2016 ◽

Vol 30 (6) ◽

pp. 471-488 ◽

Cited By ~ 2

Author(s):

Patcharapong Thangsunan ◽

Sila Kittiwachana ◽

Puttinan Meepowpan ◽

Nawee Kungwan ◽

Panchika Prangkio ◽

...

Keyword(s):

Neural Network ◽

Integrase Inhibitors ◽

Activity Prediction ◽

Artificial Neural ◽

Network Approaches ◽

Hiv 1

Quantitative EEG (qEEG) predicts relapse in patients with primary alcoholism — a comparison between multivariate statistics and artificial neural network approaches

Psychiatry Research Neuroimaging ◽

10.1016/s0925-4927(97)81603-4 ◽

1997 ◽

Vol 68 (2-3) ◽

pp. 183-184

Author(s):

G. Winterer ◽

B. Klöppel ◽

A. Heinz ◽

M. Ziller ◽

L.G. Schmidt ◽

...

Keyword(s):

Neural Network ◽

Multivariate Statistics ◽

Quantitative Eeg ◽

Artificial Neural ◽