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Related Concept Videos

State Space Representation01:27

State Space Representation

677
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
677
Transfer Function to State Space01:23

Transfer Function to State Space

917
State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an RLC...
917
Classification of Systems-II01:31

Classification of Systems-II

547
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,
547
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

457
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
457
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

393
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
393
State Space to Transfer Function01:21

State Space to Transfer Function

653
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
653

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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Parameter redundancy in discrete state-space and integrated models.

Diana J Cole1, Rachel S McCrea2

  • 1School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, Kent CT2 7NF, England. d.j.cole@kent.ac.uk.

Biometrical Journal. Biometrische Zeitschrift
|July 1, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces methods to detect parameter redundancy in discrete state-space models used in ecology. Combining data sets in integrated population models can resolve parameter non-identifiability issues.

Keywords:
Capture-recaptureIdentifiabilityJoint likelihoodMark-recovery modelsPopulation dynamics

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Area of Science:

  • Ecology
  • Mathematical Modeling

Background:

  • Discrete state-space models are crucial for understanding wild animal population dynamics.
  • Parameter redundancy, or non-identifiability, hinders the estimation of ecologically important parameters like survival probability.

Purpose of the Study:

  • To develop and propose methods for detecting parameter redundancy in discrete state-space models.
  • To investigate how integrated population models can address parameter non-identifiability.

Main Methods:

  • Developed two novel methods for deriving an exhaustive summary of parameters in discrete state-space models.
  • Utilized discrete analogues of established methods for continuous state-space models.

Main Results:

  • The proposed methods effectively detect parameter redundancy in ecological models.
  • Integrated population models, by combining multiple datasets, can lead to fully estimable parameter sets.

Conclusions:

  • The developed methods provide a robust framework for identifying non-identifiable parameters in ecological models.
  • Data integration strategies are vital for overcoming parameter redundancy and improving population model estimation.