NONLINEAR STRUCTURAL ESTIMATION OF LOCALIZED NETWORK USING HOMOTOPHIC TOPOLOGICAL (2(N)+1) DIMENSIONAL FOR DISTANCE THEORY (HTD-DT)

Abstract

A Nonlinear Structural Equation Model (NLSEM) is formed on the basis of various dimension in normal mutual estimation depending on Distance Estimation Theory (DET) and its complex networks structure. The homotophy linear topography analyze the dimension of formal network in hidden paths to consider the linear structure. However, dimension theory is a linear dependence between the variables for observation is problem in nature of distance estimation along the node and these approaches have limitations to form shortest communication. This paper proposes the Nonlinear structural estimation of localized network using homotphic topological (2 (n)+1) dimensional for distance theory Structure equation model based on the Probability distribution theory of evaluation model (PDTE) that compensates for the potential innumerable dependencies between network points. For this unstructural reason, network densities are provided to take advantage of the lower specific margins of density that are present in most real-world networks. The Gambier IV order $(y \frac{d^2 y}{dt^2} (\alpha, \beta)$, complex constant) is used to optimize the Painleve I order $(X’=X’^{(dy/dt)}y^2 + t)$ equation to derive the neighborhood singularities to estimate the distance. This computational provides an efficient integration to the diagonal gradient algorithm has been developed to estimate the SEM coefficients of polymorphic formation and therefore infer the edge structures on distance estimation. Preliminary testing of simulated data demonstrates the effectiveness of the new approach produce high estimation with lower redundancy steps of mathematical solvation.