Support vector machine-based parameter-adaptive motion prediction method

Abstract

The invention discloses a support vector machine (SVM)-based parameter-adaptive motion prediction method, which comprises the following steps of: (1) converting sample data into a standard prediction training dynamic sequence data format of an SVM, normalizing data extremums and adding a decimal omega to each normalized extremum; (2) performing three-pixel-width linear searching by utilizing a minimum mean square error principle in grids of a logarithmic space coordinate system, finding an optimal parameter combination out and obtaining an optimal prediction model; and (3) sampling flutter data in real time, sampling k flutter displacements and performing SVM real-time prediction by using the optimal prediction model obtained by the step (2) to obtain the flutter displacement. The supportvector machine-based parameter-adaptive motion prediction method provided by the invention ensures high computing accuracy and high computing efficiency, can be used in various fields of SVM-based regression fitting and prediction, avoid the complexity of conventional manual parameter adjustment and simultaneously meet requirements on accuracy and efficiency, and well realizes automation and intellectualization.

Description

technical field [0001] The invention belongs to the technical field of computer artificial intelligence, and in particular relates to a parameter adaptive motion prediction method based on a support vector machine. Background technique [0002] Motion prediction refers to the modeling and analysis of historical data to summarize the motion law and predict the future trajectory in advance. It has been important research and application in many fields, such as robot motion control, moving target search and tracking, video image processing and compression, Image stabilization technology for imaging systems, earthquake weather prediction and mobile network user location prediction, etc. The support vector machine is an effective tool for modeling and learning to realize prediction. [0003] Support Vector Machine (Support Vector Machine, SVM) is a new machine learning method proposed by Vapnik in the 1970s in combination with the VC dimension theory of statistical theory and th...

AI Technical Summary

Problems solved by technology

Early genetic algorithms, chaotic algorithms, and artificial immune methods all used parameter adaptive optimization, but these algorithms are not only complex but also targeted in terms of accuracy and applicability, not universal, and have not been widely used and recognized. Using a combination of high-precision and low-efficiency grid search and cross-validation (Cross Validation, CV) to achieve support vector machine parameter optimization

Images