Prediction of Tensile Strength Based on RBF Neural Network

Hui Jun Li; Li Zhang

doi:10.4028/www.scientific.net/AMR.476-478.1309

Paper Titles

Preparation of Al₂O₃ Nano-Particles Reinforced Fe-Based Composite Materials via In Situ Synthesis
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Point Clouds Based Dynamical Representation for Heterogeneous Objects
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Research of Ventilation and Dust Removal Technology for Whole Rock Fully-Mechanized Excavation Face
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PWM Chopper of Ultrasonic Power Supply Based on FPGA
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Prediction of Tensile Strength Based on RBF Neural Network
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First-Principles Study of Energy Band Gap of Graphene-Like B-C-N by Strain
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Synthesis of Nanocrystalline Fe₂₅Al_57.5Ni_17.5 by Mechanical Alloying
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Microwave Synthesis and Characterization of Nanoscale Lanthanum Cobaltite
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Adsorption Kinetics of Cr (VI) onto Strong Alkaline Anion Exchange Fiber from Aqueous Solutions
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 476-478Prediction of Tensile Strength Based on RBF Neural...

Prediction of Tensile Strength Based on RBF Neural Network

Abstract:

The objective of this research is to predict yarn tensile strength. The model of predicting yarn tensile strength is built based on RBF neural network. The RBF neural networks are trained with HVI test results of cotton and USTER TENSOJET 5-S400 test results of yarn. The results show prediction models based on RBF neural network are very precise and efficient.