现代电子技术Issue(19)：128-131,4.
航向数据混沌特性分析及预测
Analysis and prediction for chaotic characteristics of course data
黎才鑫 1李天伟 1郭姣 2孟凡军1
作者信息
- 1. 海军大连舰艇学院 航海系，辽宁 大连 116018
- 2. 海军大连舰艇学院 基础部，辽宁 大连 116018
- 折叠
摘要
Abstract
Since the course data from INS features chaotic property,the chaos theory is applied to its analysis and C-C method is used for reconstructing the phase space of course data. Based on this,not only different methods are adopted to con-duct qualitative and quantitative analyses of chaos characteristics,but also its maximum Lyapunov exponent is calculated. Taking the time sequence after phase space reconstruction as variable input,RBF neural network is used to predict course data,the test result shows that its prediction accuracy is superior to that of the traditional RBF neural network without the phase space re-constructing.关键词
航向数据/混沌特性分析/相空间重构/RBF神经网络/Lyapunov指数Key words
course data/chaotic characteristic analysis/phase space reconstruction/RBF neural network/Lyapunov exponent分类
信息技术与安全科学引用本文复制引用
黎才鑫,李天伟,郭姣,孟凡军..航向数据混沌特性分析及预测[J].现代电子技术,2014,(19):128-131,4.
