scholarly journals A Unified Frequency Domain Model to Study the Effect of Demyelination on Axonal Conduction

2016 ◽  
Vol 7 ◽  
pp. BECB.S38554 ◽  
Author(s):  
Saurabh Chaubey ◽  
Shikha J. Goodwin
Keyword(s):  
System Identification ◽  
Transfer Function ◽  
Frequency Domain ◽  
Linear Time ◽  
Signal Propagation ◽  
Nerve Fibers ◽  
Domain Model ◽  
Linear Time Invariant ◽  
Cable Properties ◽  
Identification Approach

Multiple sclerosis is a disease caused by demyelination of nerve fibers. In order to determine the loss of signal with the percentage of demyelination, we need to develop models that can simulate this effect. Existing time-based models does not provide a method to determine the influences of demyelination based on simulation results. Our goal is to develop a system identification approach to generate a transfer function in the frequency domain. The idea is to create a unified modeling approach for neural action potential propagation along the length of an axon containing number of Nodes of Ranvier (N). A system identification approach has been used to identify a transfer function of the classical Hodgkin-Huxley equations for membrane voltage potential. Using this approach, we model cable properties and signal propagation along the length of the axon with N node myelination. MATLAB/ Simulink platform is used to analyze an N node-myelinated neuronal axon. The ability to transfer function in the frequency domain will help reduce effort and will give a much more realistic feel when compared to the classical time-based approach. Once a transfer function is identified, the conduction as a cascade of each linear time invariant system-based transfer function can be modeled. Using this approach, future studies can model the loss of myelin in various parts of nervous system.

scholarly journals Laboratory Calibration of D-dot Sensor Based on System Identification Method

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3255 ◽  
Author(s):  
Ke Wang ◽  
Yantao Duan ◽  
Lihua Shi ◽  
Shi Qiu
Keyword(s):  
System Identification ◽  
Transfer Function ◽  
Electric Fields ◽  
Linear Time ◽  
Low Frequency ◽  
Calibration Method ◽  
Sensitivity Coefficient ◽  
Corner Frequency ◽  
Linear Time Invariant ◽  
The Time Domain

D-dot sensors can realize the non-contact measurement of transient electric fields, which is widely applied to electromagnetic pulse (EMP) measurements with characteristics of the wide frequency band, high linearity, and good stability. In order to achieve accurate calibration of D-dot sensors in the laboratory environment, this paper proposed a new calibration method based on system identification. Firstly, the D-dot sensor can be considered as a linear time-invariant (LTI) system under corner frequency, thus its frequency response can be characterized by the transfer function of a discrete output error (OE) model. Secondly, based on the partial linear regression of the transfer function curve, the sensitivity coefficient of the D-dot sensor is obtained. By increasing the influence weight of low-frequency components, this proposed method has better calibration performance when the waveform is distorted in the time domain, and can artificially adapt to the operating frequency range of the sensor at the same time.

scholarly journals Frequency-Domain Robust Performance Condition for Controller Uncertainty in SISO LTI Systems: A Geometric Approach

2009 ◽  
Vol 2009 ◽  
pp. 1-9
Author(s):  
Vahid Raissi Dehkordi ◽  
Benoit Boulet
Keyword(s):  
Frequency Domain ◽  
Linear Time ◽  
Order Reduction ◽  
Geometric Approach ◽  
Geometrical Approach ◽  
Robust Performance ◽  
Linear Time Invariant ◽  
Additive Perturbation ◽  
Plant Uncertainty ◽  
Invariant Control

This paper deals with the robust performance problem of a linear time-invariant control system in the presence of robust controller uncertainty. Assuming that plant uncertainty is modeled as an additive perturbation, a geometrical approach is followed in order to find a necessary and sufficient condition for robust performance in the form of a bound on the magnitude of controller uncertainty. This frequency domain bound is derived by converting the problem into an optimization problem, whose solution is shown to be more time-efficient than a conventional structured singular value calculation. The bound on controller uncertainty can be used in controller order reduction and implementation problems.

Fourier Series-Based Neural Networks for Vehicle System Identification

1999 ◽  
Author(s):  
Imtiaz Haque ◽  
Juergen Schuller
Keyword(s):  
Neural Networks ◽  
Fourier Series ◽  
System Identification ◽  
Transfer Function ◽  
Parameter Identification ◽  
Frequency Domain ◽  
Vehicle System ◽  
Non Linear ◽  
Function Identification ◽  
Non Linear System

Abstract The use of neural networks in system identification is an emerging field. Neural networks have become popular in recent years as a means to identify linear and non-linear systems whose characteristics are unknown. The success of sigmoidal networks in parameter identification has been limited. However, harmonic activation-based neural networks, recent arrivals in the field of neural networks, have shown excellent promise in linear and non-linear system parameter identification. They have been shown to have excellent generalization capability, computational parallelism, absence of local minima, and good convergence properties. They can be used in the time and frequency domain. This paper presents the application of a special class of such networks, namely Fourier Series neural networks (FSNN) to vehicle system identification. In this paper, the applications of the FSNNs are limited to the frequency domain. Two examples are presented. The results of the identification are based on simulation data. The first example demonstrates the transfer function identification of a two-degree-of freedom lateral dynamics model of an automobile. The second example involves transfer function identification for a quarter car model. The network set-up for such identification is described. The results of the network identification are compared with theory. The results indicate excellent prediction properties of such networks.

Identification of Multivariable Nonlinear Dynamic System Based on PID Neural Network

2015 ◽  
Vol 719-720 ◽  
pp. 475-481
Author(s):  
Hua Shu ◽  
Huai Lin Shu
Keyword(s):  
Neural Network ◽  
System Identification ◽  
Dynamic System ◽  
Nonlinear Dynamic ◽  
Linear Time ◽  
Nonlinear Dynamic System ◽  
Identification Methods ◽  
Linear Time Invariant ◽  
Linear Time Invariant Systems ◽  
Pid Neural Network

System identification is the basis for control system design. For linear time-invariant systems have a variety of identification methods, identification methods for nonlinear dynamic system is still in the exploratory stage. Nonlinear identification method based on neural network is a simple and effective general method that does not require too much priori experience about the system to be identified. Through training and learning, the network weights are corrected to achieve the purpose of system identification. The paper is about the identification of multivariable nonlinear dynamic system based on PID neural network. The structure and algorithm of PID neural network are introduced and the properties and characteristics are analyzed. The system identification is completed and the results are fast convergence.

Floquet Experimental Modal Analysis for System Identification of Linear Time-Periodic Systems

2007 ◽  
Author(s):  
Matthew S. Allen
Keyword(s):  
System Identification ◽  
Linear Time ◽  
Time Varying ◽  
Response Model ◽  
State Matrix ◽  
Linear Time Invariant ◽  
Response Data ◽  
Time Varying Systems ◽  
Time Invariant ◽  
Time Periodic

A variety of systems can be faithfully modeled as linear with coefficients that vary periodically with time or Linear Time-Periodic (LTP). Examples include anisotropic rotorbearing systems, wind turbines, satellite systems, etc… A number of powerful techniques have been presented in the past few decades, so that one might expect to model or control an LTP system with relative ease compared to time varying systems in general. However, few, if any, methods exist for experimentally characterizing LTP systems. This work seeks to produce a set of tools that can be used to characterize LTP systems completely through experiment. While such an approach is commonplace for LTI systems, all current methods for time varying systems require either that the system parameters vary slowly with time or else simply identify a few parameters of a pre-defined model to response data. A previous work presented two methods by which system identification techniques for linear time invariant (LTI) systems could be used to identify a response model for an LTP system from free response data. One of these allows the system’s model order to be determined exactly as if the system were linear time-invariant. This work presents a means whereby the response model identified in the previous work can be used to generate the full state transition matrix and the underlying time varying state matrix from an identified LTP response model and illustrates the entire system-identification process using simulated response data for a Jeffcott rotor in anisotropic bearings.

Application of an Advanced Frequency Domain Identification Method for Modelling of Flexible-Link Manipulators

2003 ◽  
Author(s):  
G. M. Y. Lai ◽  
K. Ziaei ◽  
D. W. L. Wang ◽  
G. R. Heppler
Keyword(s):  
System Identification ◽  
Transfer Function ◽  
Frequency Domain ◽  
Contact Force ◽  
Strain Gauges ◽  
Flexible Link ◽  
Domain Identification ◽  
Frequency Responses ◽  
System Frequency ◽  
Identification Tool

This paper investigates the Comprehensive Identification from FrEquency Responses (CIFER) technique as a system identification tool for the Single Flexible Link (SFL) manipulator system. Frequency responses are identified for both the constrained and unconstrained motions. For the constrained case, two sets of frequency responses are identified based on actual contact force and an approximated contact force obtained through strain gauges readings. Identification results from CIFER® are compared to those from the Empirical Transfer Function Estimate (ETFE).

A Frequency Domain Identification Scheme for Control and Fault Diagnosis

1997 ◽  
Vol 119 (1) ◽  
pp. 48-56 ◽  
Author(s):  
G. Mallory ◽  
R. Doraiswami
Keyword(s):  
Frequency Domain ◽  
Linear Time ◽  
Robot Manipulator ◽  
Physical Parameters ◽  
Linear Time Invariant ◽  
Domain Identification ◽  
Frequency Domain Identification ◽  
Identification Technique ◽  
Manipulator Arm ◽  
Linear Time Invariant System

A robust scheme to estimate a set of models for a linear time-invariant system, subject to perturbations in the physical parameters, from a frequency response data record is proposed. The true model as well as the disturbances affecting the system are assumed unknown. However, the physical parameters are assumed to enter the coefficients of the system transfer function multilinearly. A set of models is identified by perturbing the physical parameters one-at-time and using a frequency domain identification technique. Exploiting the assumed multilinearity, the estimated set of models is validated. The proposed scheme is evaluated on a number of simulated systems, and on a physical robot manipulator arm.

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