您要查找的是不是：
- Simulations show these two methods are effective for chaotic time series prediction,including ... 仿真表明,两种模型均能有效预报舰船摇荡极短期运动。
- As a new type of recurrent neural network,echo state network(ESN) is applied to nonlinear system identification and chaotic time series prediction. ESN(回声状态网络)是一种新型的递归神经网络;可有效处理非线性系统辨识以及混沌时间序列预测问题.
- Ridge regression learning in ESN for chaotic time series prediction ESN岭回归学习算法及混沌时间序列预测
- Reservoir neural state reconstruction and chaotic time series prediction 储备池状态空间重构与混沌时间序列预测
- chaotic time series prediction 混沌时间序列预测
- Simulations show that RBF networks models have good fitness and high accuracy of single and multistep prediction to the chaotic time series. 仿真结果表明,RBF网络模型对混沌时间序列有比较强的拟合能力和比较高的一步及多步预测精度。
- Zhang J S,Xiao X C.A reduced parameter second-order volterra filter with application to nonlinear adaptive prediction of chaotic time series [J].Acta Phys Sin,2001,50(7):1249. [4]张家树;肖先赐.;用于混沌时间序列自适应预测的一种少参数二阶Volterra滤波器[J]
- Based on the delay-coordinate reconstruction and bilinear expressions in the phase space of a chaotic system, a bilinear adaptive filter was designed to predict low-dimensional chaotic time series. 摘要基于混沌动力系统相空间的延迟坐标重构和双线性表达式，设计了预测混沌时间序列的双线性自适应预测滤波器。
- On the basis of chaotic time series,this paper uses the BP neural network method to forecast the power load,and analyzes the model characteristic. 在混沌时间序列的基础上,应用BP神经网络对电力负荷进行了预测,并对模型特性进行了分析。
- Based on the analysis of polynomial nonlinear adaptive prediction methods existed already, a DCT domain quadratic predictor for real-time prediction of low-dimension chaotic time series is proposed. 在分析现有多项式非线性自适应预测法的基础上，提出了混沌时间序列预测的DCT域二次实时自适应滤波预测法；
- A new method for time series prediction based on EMD and ANN is presented in this paper. 提出了一种新的时间序列预测方法:利用EMD分解法和神经网络进行信号预测。
- A new time series prediction method was put forward, based on fuzzy least square support vector machine (FLS-SVM). 摘要提出了一种基于隶属度模糊最小二乘支持向量机(FLS-SVM)的时间序列预测新方法。
- Guo S B,Xiao X C.Determining rank of Volterra adaptive filter of chaotic time series [J].Journal of Electronics and Information Technology,2002,24(10):1334-1340. [13]郭双冰;肖先赐.;混沌时间序列的Volterra自适应预测滤波器定阶[J]
- In the case of chaotic time series, using phase space reconstruction and probability raising method can extract the UPO’s embedded in the chaotic time series. 混沌时间序列的情况下，通过相空间重构技术和概率提升法可以计算嵌入于其中的不稳定周期轨道。
- In order to reduce the expense of ANN training, we have developed a dynamic neural network (DNN) modeling method for online time series prediction. 如果每增加一个样本对神经网络重新训练，需要大量的计算时间。
- In the end of this paper, the experiment to apply FCP to time series prediction shows that FCP outperforms CP and RBF in learning precision and generalizaton ability. FCP在时间序列预测中的 应用表明;FCP不仅在学习精度上;而且在泛化能力方面较之CP和RBF均有较大的改善.
- The experimental result indicates that the combined model has higher precision of prediction and better adaptability, compared with other single time series prediction models. 数据试验结果表明，与单一时间序列预测模型相比，该模型具有较高的预测精度和很好的模型适应性。
- The new method treats the problem of parameter estimation and noise reduction for chaotic time series as a nonlinear minimization process and solves it using steepest descent algorithm. 这种新方法把对混沌时间序列的参数估计和噪声抑制看作是一种最小化过程，并利用了最速梯度下降方法解决。
- In the end of this paper,the experiment to apply FCP to time series prediction shows that FCP outperforms CP and RBF in learning precision and generalizaton ability. FCP在时间序列预测中的应用表明，FCP不仅在学习精度上，而且在泛化能力方面较之CP和RBF均有较大的改善.
- In addition,GPM integrates the results from Nonlinear Time Series Analysis (NTSA) to adjust the parameters and as the criterion of founded models. The simulation shows the effectiveness of such improvements on modeling chaotic time series. 此外;演化建模的实现结合了非线性时间序列分析(NTSA)的结果;以NTSA的结果指导演化建模参数的选取并作为模型优劣的评判标准;改进了经典GP算法对混沌系统建模的应用效果.
