Using GA-ANN algorithm to predicate coal bump energy
A GA-ANN network was constructed for preidcating coal bump energy, based on the 300 training samples form simulated results with PFC2D software for different coal particle stiffness. It was tested that the average relative error of fitted-output value is only 2.5%, the averagre relative error of generalized predicated output is only 8.4%.It is valuable for coal bump energy predication.
Background ::: Anthropometric models remain appropriate alternatives to estimate body composition of peripubertal populations. However, these traditional models do not consider other body components that undergo major changes during peripubertal growth spurt, with restrictions to a multicompartimental approach as a quantitative growth. DXA has great potential to determine pediatric body composition in more than one component (3-C), but has limited use in field settings. Thus, the aim of this study was to propose and validate an anthropometric model for simultaneous estimation of lean soft tissue (LST), bone mineral content (BMC) and fat mass (FM) in healthy girls, from a multivariate approach of densitometric technique, as the criterion method.