Description: 蚁群算法的进行线性系统的辨识，一个例题。-ant colony algorithm for linear system identification, one of excellence. Platform: |
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Author:luo |
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Description: 蚁群算法的进行线性系统的辨识，一个例题。-ant colony algorithm for linear system identification, one of excellence. Platform: |
Size: 3072 |
Author:luo |
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Description: 我写了很久的线形系统模型阶次辨识,很实用的程序-I wrote a long linear system identification model order, it is practical procedures Platform: |
Size: 2048 |
Author:wjx |
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Description: 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 -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 Platform: |
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Author:kedle |
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Description: A Matlab toolbox for exact linear time-invariant system identification is presented. The emphasis is on the variety of possible ways to implement the mappings from data to parameters of the data generating system. The considered system representations are input/state/output, difference equation, and left matrix fraction.
KEYWORDS: subspace identification, deterministic subspace identification, balanced model reduction, approximate system identification, MPUM.
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Author:kedle |
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Description: 模型参考自适应控制系统由参考模型、受控对象、控制器和自适应律等组成。系统设计的核心是综合和设计控制器和自适应规律，使系统能稳定跟踪参考模型的输出[2]。近年来，对时变系统的自适应控制的研究已取得了较大的进展。在文[3]的基础上，本文针对线性时变系统的一种改进的模型参考自适应控制方案进行仿真研究，仿真结果说明了该控制方案的可行性。-Model reference adaptive control system consists of a reference model, the controlled object, controller and adaptive laws such as the composition. Is the core of system design and design of integrated controller and adaptive laws, enables the system to stabilize the output of reference model tracking [2]. In recent years, of time-varying systems of adaptive control research has been made greater progress. In the text [3] on the basis of this paper, linear time-varying systems A modified model reference adaptive control program simulation study, simulation results illustrate the feasibility of the control program. Platform: |
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Author:zl |
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Description: 利用神经网络对输入的Ｍ序列进行线性离散系统辨识，及其改进算法-The use of neural network input M-sequence of linear discrete-time system identification, and its improved algorithm Platform: |
Size: 2048 |
Author:张云鹏 |
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Description: 摘要:本文旨在研究一种能对复杂热工对象的有效建模方法。基于遗传算法的辨识方法有较强的抗干扰能力,对低、高阶系统、延时系统都可以达到很好的辨识效果。根据单元机组的低阶非线性模型,推导出一个双进双出、能够描述机组动态特性及机炉间相互耦合关系的协调控制系统传递函数矩阵。依次模型为基础,提出一种基于改进的遗传算法的参数辨识方法。-Abstract: This paper aims to study a thermal complex objects can be an effective modeling method. Identification method based on genetic algorithm has strong anti-jamming ability, low, high-end systems, delay systems can achieve very good recognition results. According to unit power plant low-level non-linear model, derived a double inlet and outlet, can describe the dynamic characteristics and boiler-turbine unit is coupled between two relations, coordination and control system transfer function matrix. Turn model was proposed based on improved genetic algorithm based on parameter identification method. Platform: |
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Author:space6 |
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Description: Developing Models from Experimental Data using System Identification Toolbox-1. webinar_walk_through.m: contains all the linear and nonlinear estimation examples presented during the webinar.
2. Data files and Simulink models: process_data.mat, ExampleModel.mdl, Friction_Model.mdl. Any other data files used in the presentation already ship with the toolbox (ver 7.0).
Products used:
- You basically need only System Identification Toolbox (SITB) to try out most examples.
- To use Simulink blocks, you would, of course, need Simulink.
- Control System Toolbox is used at one place to show how estimated models can be converted into LTI objects (SS, TF etc)
- Optimization Toolbox will be used if available for grey box estimation. If not, SITB s built-in optimizers will be used automatically.
- Other products mentioned: Neural Network Toolbox, Model Predictive Control Toolbox and Robust Control Toolbox.
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Author:陈翼男 |
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Description: 最小均方算法是一种自适应滤波算法，这里的Matlab程序用于根据LMS最新均方识别一个线性噪声系统-LMS algorithm is an adaptive filter algorithm, where the Matlab program for the latest according to the mean square LMS noise system identification of a linear Platform: |
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Author:lluu |
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Description: This addresses the use of ANFIS function in the Fuzzy Logic Toolbox for nonlinear dynamical system identification. This
also requires the System Identification Toolbox, as a comparison is made between a nonlinear ANFIS and a linear ARX model. Platform: |
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Author:manoj |
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Description: 在matlab中利用Volterra非线形滤波器进行系统辨识，并带有一个应用示例。代码带有详细注释。-In the matlab filter using Volterra non-linear system identification, and with an application example. Code with detailed comments. Platform: |
Size: 3072 |
Author:bigbigtom |
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Description: CUED系统识别工具箱，包括：
1 Subspace identification of linear systems.
2 Subspace identification of bilinear systems-CUED System Identification Toolbox:
1 Subspace identification of linear systems.
2 Subspace identification of bilinear systems Platform: |
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Author:Xijun Ye |
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Description: There are four major types of adaptive filtering configurations adaptive system identification, adaptive noise cancellation, adaptive linear prediction, and adaptive inverse system. All of the above systems are similar in the implementation of the algorithm, but different in system configuration. All 4 systems have the same general parts an input x(n), a desired result d(n), an output y(n), an adaptive transfer function w(n), and an error signal e(n) which is the difference between the desired output d(n) and the actual output y(n). In addition to these parts, the system identification and the inverse system configurations have an unknown linear system u(n) that can receive an input and give a linear output to the given input [2].-There are four major types of adaptive filtering configurations adaptive system identification, adaptive noise cancellation, adaptive linear prediction, and adaptive inverse system. All of the above systems are similar in the implementation of the algorithm, but different in system configuration. All 4 systems have the same general parts an input x(n), a desired result d(n), an output y(n), an adaptive transfer function w(n), and an error signal e(n) which is the difference between the desired output d(n) and the actual output y(n). In addition to these parts, the system identification and the inverse system configurations have an unknown linear system u(n) that can receive an input and give a linear output to the given input [2]. Platform: |
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Author:maidenfreak |
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Description: Nonlinear System Identification
This demo addresses the use of ANFIS function in the
Fuzzy Logic Toolbox(TM) for nonlinear dynamical system identification.
This demo also requires the System Identification Toolbox(TM), as a comparison is made
between a nonlinear ANFIS and a linear ARX model.
Copyright 1994-2007 The MathWorks, Inc.
$Revision: 1.9.2.4 $- Nonlinear System Identification
This demo addresses the use of ANFIS function in the
Fuzzy Logic Toolbox(TM) for nonlinear dynamical system identification.
This demo also requires the System Identification Toolbox(TM), as a comparison is made
between a nonlinear ANFIS and a linear ARX model.
Copyright 1994-2007 The MathWorks, Inc.
$Revision: 1.9.2.4 $ Platform: |
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Author:Mohammed |
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Description: This paper describes the application of an identification algorithm clustering type Gustafson-Kessel nonlinear
dynamical system. From input-output data the algorithm generates fuzzy models of Takagi-Sugeno. This type of modeling is applied
to a non-linear numerical model. The non-linear input / output model of the system is decomposed in several described by
membership functions and fuzzy rule-based local linear systems. The results are presented and prospects for future work. Platform: |
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Author:orques
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