Analysis and Application of Step Size of RK4 for Performance Measure of Predictability Horizon of Chaotic Time Series
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[1] Pengfei Wang,et al. Computational uncertainty and the application of a high-performance multiple precision scheme to obtaining the correct reference solution of Lorenz equations , 2011, Numerical Algorithms.
[2] Shuichi Kurogi,et al. Properties of Direct Multi-Step Ahead Prediction of Chaotic Time Series and Out-of-Bag Estimate for Model Selection , 2014, ICONIP.
[3] S. Liao,et al. On the mathematically reliable long-term simulation of chaotic solutions of Lorenz equation in the interval [0,10000] , 2013, 1305.4222.
[4] Shuichi Kurogi,et al. Hierarchical clustering of ensemble prediction using LOOCV predictable horizon for chaotic time series , 2017, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).
[5] Shuichi Kurogi,et al. Performance improvement via bagging in probabilistic prediction of chaotic time series using similarity of attractors and LOOCV predictable horizon , 2017, Neural Computing and Applications.
[6] Shuichi Kurogi,et al. Probabilistic Prediction of Chaotic Time Series Using Similarity of Attractors and LOOCV Predictable Horizons for Obtaining Plausible Predictions , 2015, ICONIP.
[7] Kevin Judd,et al. Time Step Sensitivity of Nonlinear Atmospheric Models: Numerical Convergence, Truncation Error Growth, and Ensemble Design , 2007 .