Parameter determining method of Gaussian radial basis function agent model

Abstract

The invention belongs to the field of information processing, and particularly relates to a parameter determining method of a Gaussian radial basis function agent model. According to the technical scheme, the parameter determining method includes the steps that sample space is linearly mapped into a unit cube; the local densities of sample points are calculated according to sample distribution conditions; the minimum distance between the sample point with the minimum local density and other sample points is calculated; the kernel widths of the sample points are determined; the weight coefficients of basis functions corresponding to the sample points are determined. The parameter determining method has the advantages that the method is clear in logic, easy and convenient to operate and easy to execute; the calculated amount is hardly increased with the basis-function kernel width determining method; the Gaussian radial basis function agent model obtained with the method can be commonly used for even / uneven samples, and the parameter determining method is reliable, efficient and high in accuracy.

Description

technical field [0001] The invention belongs to the field of information processing, and in particular relates to a method for determining parameters of a Gaussian radial basis function proxy model. Background technique [0002] The proxy model method is often involved in engineering analysis, design, and optimization processes. Its basic idea is to replace the original complex calculation analysis model with a relatively simple analytical function, and use the response information of known discrete data points (samples) to predict Unknown point response value. The radial basis function surrogate model is relatively reliable in terms of accuracy and robustness, and it is a surrogate model widely used in various engineering fields. The Gaussian function has good continuity and derivability, and is widely used as the kernel function of the radial basis function, and its expression is: [0003] [0004] In the formula, the kernel width of the σ Gaussian kernel function. ...

AI Technical Summary

Problems solved by technology

The former type of method has a small amount of calculation, and the approximate model obtained by uniformly distributed samples can achieve high accuracy, but when the sample distribution is uneven, the approximation accuracy cannot be reliably guaranteed; the latter type of method corresponds to a proxy model with relatively high accuracy. But the computation is relatively large

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