Grey bootstrap and unascertained rational number-based small sample test data estimation method

Abstract

The invention discloses a grey bootstrap and unascertained rational number-based small sample test data estimation method. The method comprises the following steps of S1, generating grey bootstrap ofsmall sample test data; S2, constructing and optimizing an unascertained rational number: establishing the unascertained rational number, and when a credibility entropy is a maximum value, estimatingan optimal order of the unascertained rational number, thereby realizing optimization, wherein the larger the credibility entropy of the established unascertained rational number is, the better the description of test indexes is; and S3, performing parameter estimation based on the unascertained rational number. According to the grey bootstrap and unascertained rational number-based small sample test data estimation method, point estimation, interval estimation and estimation reliability models are given, and example verification is performed; and a simulation example shows that the method isreasonable and feasible, the parameter estimation problem of equipment test data can be effectively solved, and a probability distribution feature of original data is not needed.

Description

technical field [0001] The invention relates to a small sample test data estimation method based on gray bootstrap and unascertained rational numbers. Background technique [0002] The test and appraisal of weaponry and equipment is an important link in the life-cycle management of weaponry and equipment. With the development of networking, systematization and intelligence of weaponry and equipment, the purpose of the test is complex and diverse, and the cost of the test is getting higher and higher, which makes the test and appraisal more and more difficult. Usually only a small amount of field confrontation tests can be carried out [1]. Parameter estimation of small sample test data and evaluation based on small sample test data have become key problems that need to be solved urgently in the field of equipment test identification. [0003] The current small sample data processing mainly adopts two ideas, one is the probability statistics method, including the classic stati...

AI Technical Summary

Problems solved by technology

[0007] According to the technical problems raised above, a method for estimating small-sample experimental data based on gray bootstrapping and unascertained rational numbers is provided, which is used to solve the existing parameter estimation of small-sample experimental data and evaluation techniques based on small-sample experimental data. Disadvantages of not being able to meet the requirements in the field of equipment testing and identification

Images