Bio-inspired computational heuristics for parameter estimation of nonlinear Hammerstein controlled autoregressive system-Reference-Cited by-同舟云学术

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Bio-inspired computational heuristics for parameter estimation of nonlinear Hammerstein controlled autoregressive system

Published:2016-11-11 Issue:12 Volume:29 Page:1455-1474
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Raja Muhammad Asif Zahoor,Shah Abbas Ali,Mehmood Ammara,Chaudhary Naveed Ishtiaq,Aslam Muhammad Saeed

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/article/10.1007/s00521-016-2677-x/fulltext.html

Reference58 articles.

1. Corbier C, El Badaoui M, Ugalde HMR (2015) Huberian approach for reduced order ARMA modeling of neurodegenerative disorder signal. Signal Process 113:273–284

2. Ding F, Wang Y, Ding J (2015) Recursive least squares parameter identification algorithms for systems with colored noise using the filtering technique and the auxilary model. Digit Signal Process 37:100–108

3. Raja MAZ, Chaudhary NI (2014) Adaptive strategies for parameter estimation of Box–Jenkins systems. IET Signal Process 8(9):968–980

4. Zhang T, Qian W, Zhang G, Ye F, Gao C, Zhao H (2016) Parameter estimation of MC-CDMA signals based on modified cyclic autocorrelation. Digit Signal Process 54:46–53

5. Chaudhary NI, Raja MAZ, Aslam MS, Ahmed N (2016) Novel generalization of Volterra LMS algorithm to fractional order with application to system identification. Neural Comput Appl. doi: 10.1007/s00521-016-2548-5

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