This paper focuses on modelling fuzzy numbers with meaningful membership functions. More precisely, it proposes a method to construct trapezoidal fuzzy number approximations from raw discrete data. In many applications, input information is numerical, and therefore, particular fuzzy sets, such as fuzzy numbers, hold great interest and relevance in managing data imprecision and vagueness. The pr...

Abstract With the development of modern surveying and mapping technology, 3D laser scanners are increasingly used for building dip scanning. Building inclination characteristics analyzed using three-dimensional point cloud data binary linear regression. It can easily intuitively obtain building. This method extracted Angle from point, line, plane, surface, other angles. Then, a matching based o...

When the observed data are imprecise, uncertain regression model is more suitable for linear analysis. Least squares estimation can fully consider given and minimize sum of residual error effectively solve equation imprecisely data. On basis uncertainty theory, this paper presents an deformation method solving unknown parameters in equations. We first establish one-dimensional then extend it to...

In other words, E [y | {x1, . . . , xn}] is the best linear predictor of y based on {x1, . . . , xn}. In order for this projection to be well defined, y must have finite variance. The definition obviously can be interpreted as applying to random vectors y and xj also. Here are some useful properties of E . i. E is linear, meaning E [aX + bY |Z] = aE [X |Z] + bE [Y |Z], where a and b are numbers...

Variation in numbers of land plant species on islands in the Galapagos Archipelago can be predicted on the basis of elevation, area of the adjacent island, distance from the nearest island, and distance from the center of the archipelago, but not on the basis of the area of the host island. Multiple linear regression (y = bx(1) + bx(2) . . .) gives better "goodness of fit" than curvilinear anal...