Pareto Frontier for the time-energy cost vector to an Earth-Moon transfer orbit using the patched-conic approximation

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

The Priestley-Taylor parameter α for the Amazon forest

Priestley and Taylor provided a practical formulation of the partitioning of net radiation between heat flux and evaporation contained within a parameter α. Their model (PTM) needs verification under a range of environmental conditions. Micrometeorological data sets collected over the Amazon forest at the Ducke Reserve site (2°57′S; 59°57′W) gave an opportunity to evaluate α. Evidence presented here and by others shows that there is pronounced diurnal variation in α, with minimum values around midday and maximum values in the morning and evening hours. During unstable and stable conditions in the daylight hours, the Bowen ratio (B) varied from 0.10 to 0.57 and -0.71 to -0.08, respectively, whereas α varied from 0.67 to 1.16 and 1.28 to 3.12, respectively. A mean value of α = 1.16±0.56 was obtained from daytime hourly values for two days. The daily data sets from three expeditions gave a mean of α = 1.03±0.13. This work confirms that α is a function of atmospheric stability over the Amazon forest. Thus the PTM should be applied with caution over time-intervals of one day or less because of the sensitivity to variation in α. The calculated values of α are in general agreement with those reported in literature. © 1991.

A radial basis function network (RBFN) for function approximation

A radial basis function network (RBFN) circuit for function approximation is presented. Simulation and experimental results show that the network has good approximation capabilities. The RBFN was a squared hyperbolic secant with three adjustable parameters amplitude, width and center. To test the network a sinusoidal and sine function,vas approximated.

Comparative study between powers of sigmoid functions, MLP-backpropagation and polynomials in function approximation problems

Function approximation is a very important task in environments where the computation has to be based on extracting information from data samples in real world processes. So, the development of new mathematical model is a very important activity to guarantee the evolution of the function approximation area. In this sense, we will present the Polynomials Powers of Sigmoid (PPS) as a linear neural network. In this paper, we will introduce one series of practical results for the Polynomials Powers of Sigmoid, where we will show some advantages of the use of the powers of sigmiod functions in relationship the traditional MLP-Backpropagation and Polynomials in functions approximation problems.

Taylor's power law for ecological communities: an explanation on nonextensive/nonlinear statistical grounds

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)