System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Technometrics
Signal Processing - Fractional calculus applications in signals and systems
Signal Processing - Fractional calculus applications in signals and systems
Complex-order dynamics in hexapod locomotion
Signal Processing - Fractional calculus applications in signals and systems
Automatica (Journal of IFAC)
Digital Signal Processing
LMI stability conditions for fractional order systems
Computers & Mathematics with Applications
Calculation of all stabilizing fractional-order PD controllers for integrating time delay systems
Computers & Mathematics with Applications
Brief paper: Pseudo-state feedback stabilization of commensurate fractional order systems
Automatica (Journal of IFAC)
Studies on fractional order differentiators and integrators: A survey
Signal Processing
IEEE Transactions on Signal Processing
On distributed order integrator/differentiator
Signal Processing
Analysis of nonlinear dynamics and chaos in a fractional order financial system with time delay
Computers & Mathematics with Applications
Computers & Mathematics with Applications
Low-complexity multipath diversity through fractional sampling in OFDM
IEEE Transactions on Signal Processing
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
Approximation of high order integer systems by fractional order reduced-parameters models
Mathematical and Computer Modelling: An International Journal
An effective analytical criterion for stability testing of fractional-delay systems
Automatica (Journal of IFAC)
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When modeling a linear system in a parametric way, one needs to deal with (i) model structure selection, (ii) model order selection as well as (iii) an accurate fit of the model. The most popular model structure for linear systems has a rational form which reveals crucial physical information and insight due to the accessibility of poles and zeros. In the model order selection step, one needs to specify the number of poles and zeros in the model. Automated model order selectors like Akaike@?s Information Criterion (AIC) and the Minimum Description Length (MDL) are popular choices. A large model order in combination with poles and zeros lying closer to each other in frequency than the frequency resolution indicates that the modeled system exhibits some fractional behavior. Classical integer order techniques cannot handle this fractional behavior due to the fact that the poles and zeros are lying to close to each other to be resolvable and not enough data is available for the classical integer order identification procedure. In this paper, we study the use of fractional order poles and zeros and introduce a fully automated algorithm which (i) estimates a large integer order model, (ii) detects the fractional behavior, and (iii) identifies a fractional order system.