Spikes can be taken as absolute quantities of measuring values which are large than approximately four (expressed as variable [Times_SD] in the program)times of the standard deviation of the time series, and can be removed by repeating 3 times with each time series. When a measuring value with the deviation from the mean larger than four times of the standard deviation, the variable can be taken as NO_VALUE, and the number of spikes is saved into the variable [SpikeNum]. If the variable [Times_SD] is taken as four, many records will be removed, so the variable [Times_SD] can be taken as larger, for example eight.
標簽： approximately quantities measuring absolute
上傳時間： 2015-11-09
上傳用戶：yangbo69
State_space_reconstruction_parameters_in_the_analysis_of_chaotic_time_series_-_the_role_of_the_time_window_length. It is used for reconstruction of state space in chaotic time series, and also how to determine time window.
標簽： State_space_reconstruction_parame ters_in_the_analysis_of_chaotic_t the_role_of_
上傳時間： 2013-12-21
上傳用戶：fandeshun
Nonlinear_dynamics_delay_times_and_embedding_windows. How to determine embedded window for chaotic state space of time series
標簽： Nonlinear_dynamics_delay_times_an d_embedding_windows determine embedded
上傳時間： 2016-02-21
上傳用戶：tianyi223
The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order
標簽： identification considered features separati
上傳時間： 2016-09-20
上傳用戶：FreeSky
This a GUI that manages DSP analysis functions for wav-files (e.g., speech signals). Two functions (plotps.m and spect.m) are included for starters. You may write your own functions and integrate that into the GUI without much hassle (see instructions in the accompanying readme.txt file). Additional features like the snipper lets you trim the time series and save it as a separate wav-file. The GUI is a great tool for instructors in a DSP course and DSP researchers alike!
標簽： functions wav-files analysis manages
上傳時間： 2016-11-09
上傳用戶：從此走出陰霾
Features a unique program to estimate the power spectral density. The spectrum containing all significant details is calculated from a time series model. Model type as well as model order are determined automatically from the data, using statistical criteria. Robust estimation algorithms and order selection criteria are used to obtain reliable results. Unlike in FFT analysis, where the experimenter has to set the amount of smoothing of the raw FFT, the right level of detail is assessed using the data only.
標簽： containing Features estimate spectral
上傳時間： 2014-02-09
上傳用戶：daguda
1、該工具箱包括了混沌時間序列分析與預測的常用方法,有: (1)產生混沌時間序列(chaotic time series) Logistic映射 - \ChaosAttractors\Main_Logistic.m Henon映射 - \ChaosAttractors\Main_Henon.m Lorenz吸引子 - \ChaosAttractors\Main_Lorenz.m Duffing吸引子 - \ChaosAttractors\Main_Duffing.m Duffing2吸引子 - \ChaosAttractors\Main_Duffing2.m Rossler吸引子 -
標簽： matlab，GP，分維
上傳時間： 2015-03-02
上傳用戶：吳相澎peng
時間序列多重分形分析，matlab語言。Time series multifractal analysis, MATLAB language.
上傳時間： 2020-12-31
上傳用戶：
Philips ARM KPC2xxx series Real Time Operating System. Premptive System.
標簽： System Operating Premptive Philips
上傳時間： 2015-09-02
上傳用戶：shanml
A .zip file contains a series of scripts that were used in the MathWorks webinar "Using MATLAB to Develop Portfolio Optimization Models." The scripts generate 3D efficient frontiers for a universe of 44 stocks with time as the third axis. Additional scripts perform various ex-ante and ex-post analyses. Results are generated with and without market adjustments in the data. A readme.txt. file in the .zip folder describes each script and how to use it
標簽： MathWorks contains scripts webinar
上傳時間： 2014-01-04
上傳用戶：trepb001
