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Design of sign fractional optimization paradigms for parameter estimation of nonlinear Hammerstein systems
- Source :
- Neural Computing and Applications. 32:8381-8399
- Publication Year :
- 2019
- Publisher :
- Springer Science and Business Media LLC, 2019.
-
Abstract
- Fractional calculus plays a fundamental role in understanding the physics of nonlinear systems due to its heritage of uncertainty, nonlocality and complexity. In this study, novel sign fractional least mean square (F-LMS) algorithms are designed for ease in hardware implementation by applying sign function to input data and estimation error corresponding to first and fractional-order derivative terms in weight update mechanism of the standard F-LMS method. Theoretical expressions are derived for proposed sign F-LMS and its variants; strength of methods for different fractional orders is evaluated numerically through computer simulations for parameter estimation problem based on nonlinear Hammerstein system for low and high signal–noise variations. Comparison of the results from true parameters of the model illustrates the worth of the scheme in terms of accuracy, convergence and robustness. The stability and viability of design methodologies are examined through statistical observations on sufficiently large number of independent runs through mean square deviation and Nash–Sutcliffe efficiency performance indices.
- Subjects :
- 0209 industrial biotechnology
Estimation theory
Sign function
02 engineering and technology
Stability (probability)
Fractional calculus
Least mean squares filter
Nonlinear system
020901 industrial engineering & automation
Artificial Intelligence
Robustness (computer science)
0202 electrical engineering, electronic engineering, information engineering
Applied mathematics
020201 artificial intelligence & image processing
Software
Sign (mathematics)
Subjects
Details
- ISSN :
- 14333058 and 09410643
- Volume :
- 32
- Database :
- OpenAIRE
- Journal :
- Neural Computing and Applications
- Accession number :
- edsair.doi...........3d92c0a5aaa58493042b8573925e8adc