欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
分析了基于最大李亚普诺夫指数的混沌时间序列预测方法，提出了一种选取重构相空间中的多个邻近参考向量来提高预测精度的改进方法．对洛伦兹混沌系统产生的时间序列进行了预测，结果表明改进方法比原方法的预测精度要高,讨论了噪声和参考邻近点数对预测结果的影响．应用改进方法预测实际的交通流量时间序列的平均相对误差在8％以下，说明了改进方法的有效性．
英文摘要：
Methods for forecasting chaotic time series based on maximum Lyapunov exponent are analyzed, and an improved method is proposed, in which several neighboring reference vectors are selected in reconstructed phase space to increase forecasting precision. Time series of chaotic Lorenz system are forecasted, and the results show that the improved method has a higher accuracy than the original method, meanwhile the influence from noise and number of nearly neighbor vector on the forecasting results is discussed. The average relative error which is forecasted with real traffic volume time series by the improved method is less than 8%, which verifies the effectiveness of the improved method.
同期刊论文项目
同项目期刊论文
Explaining traffic patterns at on-ramp vicinity by a driver perception model in the framework of thr
An ILC scheme for a class of nonlinear continuous-time systems with time-iteration-varying parameter
Data-driven model-free adaptive iterative learning control for a class of discrete-time nonlinear sy
H-infinity Filtering for Markovian Switching Genetic Regulatory Networks with Time-Delays and Stocha
Discrete-time adaptive ILC for non-parametric uncertain nonlinear systems with iteration-varying tra
A data-driven iterative feedback tuning approach of ALINEA for freeway traffic ramp metering with PA
Coordinated iterative learning control schemes for train trajectory tracking with overspeed protecti
On the Intrinsic Concordance between the Wide Scattering Feature of Synchronized Flow and the Empiri
位置：