Fast system identification algorithm for non-uniformly sampled noisy biomedical signal

2002 ◽  
Author(s):  
Koon-Poon Wong ◽  
Dagan Feng ◽  
Wan-Chi Siu
Keyword(s):  
System Identification ◽  
Identification Algorithm ◽  
Biomedical Signal ◽  
Fast System

scholarly journals Nonlinear Aeroelastic System Identification Based on Neural Network

2018 ◽  
Vol 8 (10) ◽  
pp. 1916
Author(s):  
Bo Zhang ◽  
Jinglong Han ◽  
Haiwei Yun ◽  
Xiaomao Chen
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
System Identification ◽  
Control Surface ◽  
Identification Algorithm ◽  
Aeroelastic System ◽  
Structural Nonlinearity ◽  
Time Marching ◽  
Artificial Neural Network Ann ◽  
Trained Neural Network

This paper focuses on the nonlinear aeroelastic system identification method based on an artificial neural network (ANN) that uses time-delay and feedback elements. A typical two-dimensional wing section with control surface is modelled to illustrate the proposed identification algorithm. The response of the system, which applies a sine-chirp input signal on the control surface, is computed by time-marching-integration. A time-delay recurrent neural network (TDRNN) is employed and trained to predict the pitch angle of the system. The chirp and sine excitation signals are used to verify the identified system. Estimation results of the trained neural network are compared with numerical simulation values. Two types of structural nonlinearity are studied, cubic-spring and friction. The results indicate that the TDRNN can approach the nonlinear aeroelastic system exactly.

Research of Fan System Identification Based on Improved Bayes Algorithm

2011 ◽  
Vol 66-68 ◽  
pp. 448-453
Author(s):  
Hai Tao Wang ◽  
Ze Zhang
Keyword(s):  
System Identification ◽  
Dynamic Characteristics ◽  
System Modeling ◽  
Estimation Algorithm ◽  
Parametric Model ◽  
Testing System ◽  
Identification Algorithm ◽  
Environment Protection ◽  
Parameter Estimation Algorithm ◽  
Bayes Algorithm

In every filed of natural science, more and more researchers attach importance to system quantitative analysis, control and prediction. In filed of automatic control, system identification is the extension of system dynamic characteristics testing. System modeling is the basis of system identification, non-parametric model can be obtained by means of dynamic characteristics testing, but parametric model must be established by means of parameter estimation algorithm, which is more prevalent than dynamic characteristics testing. Coal power plant produces more gas and dust, so how to control the fan system plays a very important role in environment protection. We must clarify the parameter of fan system before controlling it. The traditional Bayes identification algorithm is used widely in research and industry, and the effect is relatively good. The paper induces the concept of loss function based on traditional Bayes identification algorithm, and proposes an improved Bayes identification algorithm, which can be applied to fan system identification successfully.

Analysis of a Platform for Measuring Moments and Products of Inertia of Large Vehicles

1976 ◽  
Vol 98 (2) ◽  
pp. 186-195
Author(s):  
D. Orne ◽  
T. Schmitz
Keyword(s):  
System Identification ◽  
Test Data ◽  
Normal Modes ◽  
Free Vibration Analysis ◽  
Identification Algorithm ◽  
Test Configuration ◽  
Truncation Errors ◽  
Sample Test ◽  
Instantaneous Center ◽  
Cable Lines

A rigid platform symmetrically supported by four sloping cables is proposed for measuring the center-of-gravity coordinates and the moments and products of inertia of large vehicles such as buses, trucks, and trailers. In addition to a torsional degree-of-freedom, the system undergoes pitch and roll motions about axes through the system instantaneous center which lies directly below the center of the platform at the intersection of the cable lines-of-action under quiescent conditions. The natural frequencies and normal modes of the freely vibrating loaded platform are used as inputs to a linearized System Identification Algorithm for computing the inertia properties of the test vehicle. Hypothetical test data generated from the Free Vibration Analysis of a sample test configuration are used to evaluate the sensitivity of the System Identification Algorithm to inaccuracies in test data or to truncation errors in computation.

Fast system identification using prominent subspace LMS

2014 ◽  
Vol 27 ◽  
pp. 44-56 ◽  
Author(s):  
Rongshan Yu ◽  
Ying Song ◽  
Milashini Nambiar
Keyword(s):  
System Identification ◽  
Fast System

scholarly journals Adaptive Predictive Functional Control of X-Y Pedestal for LEO Satellite Tracking Using Laguerre Functions

2021 ◽  
Vol 11 (21) ◽  
pp. 9794
Author(s):  
Reza Dadkhah Tehrani ◽  
Hadi Givi ◽  
Daniel-Eugeniu Crunteanu ◽  
Grigore Cican
Keyword(s):  
System Identification ◽  
Satellite Tracking ◽  
Identification Algorithm ◽  
Laguerre Functions ◽  
Point Change ◽  
Nonlinear Characteristics ◽  
Predictive Functional Control ◽  
Functional Control ◽  
Adaptive Control Algorithm ◽  
Leo Satellite

In this paper, Predictive Functional Control (PFC) is used for X-Y pedestal control for LEO satellite tracking. According to the nonlinear characteristics of the X-Y pedestal and pedestal model variation caused by its operating point change, the use of system identification algorithm, which is based on special types of orthonormal functions known as Laguerre functions, is presented. This algorithm is combined with PFC to obtain a novel adaptive control algorithm entitled Adaptive Predictive Functional Control (APFC). In this combination, Laguerre functions are utilized for system identification, while the PFC is the control law. An interesting feature of the proposed algorithm is its desirable performance against the interference effect of channel X and channel Y. The proposed APFC algorithm is compared with Proportional Integral Derivative (PID) controller using simulation results. The results confirm that the proposed controller improves the performance in terms of the pedestal model variations; that is, the controller is capable of adapting to the model changes desirably.

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