System Identification , Time Series
نویسندگان
چکیده
ii Captain Toolbox CAPTAIN is a MATLAB ® compatible toolbox for non stationary time series analysis, system identification, signal processing and forecasting, using unobserved components models, time variable parameter models, state dependent parameter models and multiple input transfer function models. CAPTAIN also includes functions for true digital control. iii Toolbox Installation CAPTAIN is usually distributed as a mixture of pre-parsed MATLAB ® pseudo-code (P-files) and conventional M-files. The following installation instructions assume MATLAB ® itself is already installed. 1 Copy all the M-and P-files to a directory where you want the toolbox to reside, such as Program Files\Matlab\Toolbox\Captain or similar. 2 Start MATLAB ® and add the above location of the toolbox to your path. You can use the standard addpath function or the graphical user interface to do this. Refer to your MATLAB ® documentation for more information. 3 Once installed, typing captdemo in the MATLAB ® Command Window starts a simple graphical user interface for access to the on-line demos. If this does not work, then check that you have correctly added the toolbox location to your MATLAB ® path. 4 To obtain a full list of CAPTAIN functions, type help captain in the MATLAB ® Command Window, replacing captain with the name of the installation directory chosen in item 1 above. To uninstall CAPTAIN, simply delete the files and remove the associated path.
منابع مشابه
New optimized model identification in time series model and its difficulties
Model identification is an important and complicated step within the autoregressive integrated moving average (ARIMA) methodology framework. This step is especially difficult for integrated series. In this article first investigate Box-Jenkins methodology and its faults in detecting model, and hence have discussed the problem of outliers in time series. By using this optimization method, we wil...
متن کاملReliability Analysis of Three Elements Series and Parallel Systems under Time-varying Fuzzy Failure Rate
Reliability is the most important performance issue in the engineering design process but in the real world problems, there are limitations for using the conventional reliability. Fuzzy logic has proved to be effective in expressing uncertainties in different fields, including reliability engineering. In this paper, For both the series and parallel systems composed of three identical or differe...
متن کاملSunspot series prediction using adaptive identification
In this paper a parallel and adaptive methodology for optimizing the time series prediction using System Identification is shown. In order to validate this methodology, a set of time series based on the sun activity measured during the 20th century have been used. The prediction precision for short and long term improves with this technique when it is compared with the found results using Syste...
متن کاملTime Series Identification Methodology Using Wireless Sensor Networks
Wireless sensor networks being a collection of numerous sensor nodes, each with sensing (temperature, humidity, sound level, light intensity, magnetism, etc.) and wireless communication capabilities, provide huge opportunities for monitoring and mathematical modeling of the time-evolution of the physical quantities under investigation. Starting from the measurements collected by the sensor node...
متن کاملOptimal Hankel-norm Identification of Dynamical Systems
The problem of optimal approximate system identification is addressed with a newly defined measure of misfit between observed time series and linear time-invariant models. The behavioral framework is used as a suitable axiomatic setting for a oonparametric introduction of system complexity and a notion of misfit of dynamical systems which is independent of system representations. The misfit fun...
متن کاملIdentification of outliers types in multivariate time series using genetic algorithm
Multivariate time series data, often, modeled using vector autoregressive moving average (VARMA) model. But presence of outliers can violates the stationary assumption and may lead to wrong modeling, biased estimation of parameters and inaccurate prediction. Thus, detection of these points and how to deal properly with them, especially in relation to modeling and parameter estimation of VARMA m...
متن کامل