The Non-equidistant Multivariable New Information Optimization NMGRM (1,n) Based on New Information Background Value Constructing

Abstract

Applying the principle in which new information should be used fully and modeling method of Grey system for the problem of lower precision as well as lower adaptability in non-equidistant multivariable MGM(1,n)model, taking the mean relative error as objective function, and taking the modified values of response function initial value as design variables, based on accumulated generating operation of reciprocal number, a non-equidistant multivariable new information optimization MGRM(1,n) model was put forward which was taken the mth component as the initialization. Based on index characteristic of grey model, the characteristic of integral and new information principle, the new information background value in non-equidistant multivariable new information optimization MGRM(1,n) was researched and the discrete function with non-homogeneous exponential law was used to fit the accumulated sequence and the formula of new information background value was given. The new information optimization MGRM(1,n) model can be used in non-equal interval & equal interval time series and has the characteristic of high precision as well as high adaptability. Example validates the practicability and reliability of the proposed model.

DOI: http://dx.doi.org/10.11591/telkomnika.v11i3.2187