Predicting the mechanical properties of glass fiber reinforced polymers via artificial neural network and adaptive neuro-fuzzy inference system

Computational Materials Science

Volumn 58, Issue , 2012, Pages 31-37

  Fazilat, H ^a   Ghatarband, M ^a   Mazinani, S ^b   Asadi, Z A ^b   Shiri, M E ^b   Kalaee, M R ^c  

^a ISLAMIC AZAD UNIVERSITY   (Iran)

^b AMIRKABIR UNIVERSITY OF TECHNOLOGY   (Iran)

^c QOM UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Adaptive neuro fuzzy inference system; Artificial neural network; Fuzzy inference system; Mechanical properties; Polyamide 6; Polymer composite

Indexed keywords

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; EXPERIMENTAL DATA; FEEDING RATE; FIBER REINFORCED; FUZZY INFERENCE SYSTEM; GLASS FIBER REINFORCED POLYMER; IZOD IMPACT STRENGTH; MECHANICAL PERFORMANCE; MIXING TEMPERATURE; MODELING RESULTS; MULTIPLE INPUTS SINGLE OUTPUTS; POLYAMIDE 6; POLYMER COMPOSITE; ROOT MEAN SQUARED ERRORS; SCREW SPEED; SHORT GLASS FIBER;

ETHYLENE; GLASS FIBERS; MECHANICAL PROPERTIES; NEURAL NETWORKS; POLYMERS; PROPYLENE;

FUZZY SYSTEMS;

EID: 84858974195     PISSN: 09270256     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.commatsci.2012.01.012     Document Type: Article

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