Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Classification of Systems-II

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,

Linear time-invariant Systems

Linear time-invariant Systems

A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be calculated...

Second Order systems II

Second Order systems II

In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
If ζ...

Basic Continuous Time Signals

Basic Continuous Time Signals

Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...

Continuous -time Fourier Transform

Continuous -time Fourier Transform

The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...

Sampling Continuous Time Signal

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Inflammatory and metabolic markers and comorbidities remission following sleeve gastrectomy: A single center one-year cohort study.

Primary care diabetes·2023

Same author

Case report: Diagnosis and emergency surgery on a young patient with extensive aortic dissection without any risk factors.

BMC cardiovascular disorders·2021

Same author

Long-Term Intraocular Pressure Changes after Pars Plana Vitrectomy: An 8-Year Study.

Journal of current ophthalmology·2021

Same author

Traumatic optic neuropathy treatment trial (TONTT): open label, phase 3, multicenter, semi-experimental trial.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie·2017

Same author

A comparative study on visual and optical performance of Akreos AO and Kontur AB IOLs after phacoemulsification cataract surgery.

Journal of current ophthalmology·2016

Same author

Changes in Corneal Thickness and Specular Microscopic Indices in Acute Unilateral Anterior Uveitis.

Ocular immunology and inflammation·2016

Search research articles

Related Experiment Video

Updated: Jul 6, 2026

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

System identification of two-dimensional continuous-time systems using wavelets as modulating functions.

Mahdiye Sadat Sadabadi¹, Masoud Shafiee, Mehdi Karrari

¹Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran. mahdiye.sadabadi@gmail.com

ISA Transactions

|April 12, 2008

Summary

This study introduces a new method for identifying parameters in two-dimensional continuous-time systems using modulating functions. The approach simplifies partial differential equations into algebraic ones for efficient parameter estimation.

More Related Videos

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

Published on: January 17, 2025

Related Experiment Videos

Last Updated: Jul 6, 2026

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

Published on: January 17, 2025

Area of Science:

Control Systems Engineering
Applied Mathematics
Signal Processing

Background:

Parameter identification is crucial for understanding and controlling complex dynamic systems.
Traditional methods often require knowledge of initial or boundary conditions, limiting their applicability.
Two-dimensional (2D) systems present unique challenges in parameter estimation due to their complexity.

Purpose of the Study:

To propose an effective method for parameter identification of 2D continuous-time systems.
To utilize 2D modulating functions, specifically trigonometric functions and sine-cosine wavelets, for system analysis.
To convert complex partial differential equations into simpler, linear algebraic equations for parameter estimation.

Main Methods:

Employing 2D modulating functions (trigonometric functions, sine-cosine wavelets) to transform system dynamics.
Converting 2D continuous-time systems described by partial differential equations into algebraic equations linear in parameters.
Utilizing least squares algorithms for parameter estimation.
Leveraging the 2D fast Fourier transform (FFT) algorithm for efficient computation.

Main Results:

The proposed method successfully converts 2D partial differential equations into parameter-linear algebraic equations.
Parameter estimation is achieved efficiently using least squares algorithms without needing initial or boundary conditions.
Numerical simulations validate the effectiveness and accuracy of the developed parameter identification algorithm.
The use of 2D FFT significantly speeds up the underlying computations.

Conclusions:

The presented method offers a robust and efficient approach for parameter identification in 2D continuous-time systems.
The technique eliminates the need for estimating unknown initial or boundary conditions, simplifying the process.
The proposed algorithm demonstrates significant potential for applications in various fields involving 2D system modeling and control.