Search

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Cyber Security & Data Governance is the property of Editorial Office of Information Technology & Network Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A combined prediction method based on hidden Murkov model (HMM) and least square sup­port vector machine (LS-SVM) is presented for prediction of avionics stales. Multi-agent genetic algo­rithm (MACA) is used to estimate HMM parameters to overcome the problem of that Baum-Weleh algo­rithm is easy to full into local optimal solution. The conditional probability of stales is introduced into the HMM modeling to reduce the effect of uncertainty factor. MACA is used lo estimate the parameters of LS-SVM model, and the pruning algorithm is used for achieving the sparse approximation of LS-SVM in pa­rameter estimation, thus achieving the objective of improving the generalization performance of LS-SVM. On this basis, a combined prediction model of avionics state is established. The analysis results show the combined prediction model has high prediction accuracy, calculating speed and stability. [ABSTRACT FROM AUTHOR]