Prediction of gas solubility in polymers by back propagation artificial neural network based on self-adaptive particle swarm optimization algorithm and chaos theory

Fluid Phase Equilibria

Volumn 356, Issue , 2013, Pages 11-17

  Li, Mengshan ^a,b   Huang, Xingyuan ^a   Liu, Hesheng ^a,c   Liu, Bingxiang ^b   Wu, Yan ^b   Xiong, Aihua ^c   Dong, Tianwen ^c  

^a NANCHANG UNIVERSITY   (China)

^b JINGDEZHEN CERAMIC INSTITUTE   (China)

^c SHANGRAO NORMAL UNIVERSITY   (China)

Author keywords

Artificial neural network; Chaos; Gas in polymers; Particle swarm optimization; Solubility prediction

Indexed keywords

BACK PROPAGATION ARTIFICIAL NEURAL NETWORK (BPANN); BACK-PROPAGATION ARTIFICIAL NEURAL NETWORK; MODIFIED PSO ALGORITHMS; PARTICLE SWARM OPTIMIZATION ALGORITHM; PRE-MATURE CONVERGENCES; SOLUBILITY PREDICTION; SQUARED CORRELATION COEFFICIENTS; TEMPERATURE AND PRESSURES;

ALGORITHMS; BACKPROPAGATION; CARBON DIOXIDE; CHAOS THEORY; FORECASTING; GASES; NEURAL NETWORKS; PARTICLE SWARM OPTIMIZATION (PSO); POLYPROPYLENES; POLYSTYRENES; SOLUBILITY;

POLYMERS;

EID: 84884166181     PISSN: 03783812     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.fluid.2013.07.017     Document Type: Article

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