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Comparison On The Performance Between The Three Structures of IIR Adaptive Filter For System Identification Based On Genetic Algorithms (GA).

Author
Shao, Xin
Published
[University Park, Pennsylvania] : Pennsylvania State University, 2015.
Physical Description
1 electronic document
Additional Creators
Jenkins, Kenneth W.
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Open Access.
Summary
Genetic Algorithms (GA) are based on the principles of natural selection and natural genetics that originate in biology. The Genetic Algorithm (GA) has been used for IIR adaptive system identification to deal with its multimodal error surface. The Genetic Algorithm (GA) can be very useful in the all three structures of IIR filters while the Gradient Algorithm experiences many difficulties due to the recursive feedback. This thesis will focus on the different performances on three structures of IIR adaptive filters based on the Genetic Algorithm (GA) and Multi-Parents Genetic Algorithm (MPGA). Experimental results demonstrate that, in general, the standard Genetic Algorithm (GA) direct form will have lower Mean Square Error (MSE), while the cascade and parallel forms will have higher convergence rates. The relative performance of three structures for Multi-Parents Genetic Algorithm (MPGA) is similar to the 2-parent Genetic Algorithm, but the rate of convergence is higher than the standard GA, which means the MPGA converges faster than the standard GA. Furthermore the performances of the three structures for the IIR filter based on modified Multi-Parents Genetic Algorithm (MPGA) are very similar. Simulation results demonstrate that when compared with the GA, the MPGA operates similarly on the three different structures, increases the rate of convergence rate and reduces the computational complexity. Finally, the Genetic Algorithm and the Gradient Algorithm were combined on the direct form to take advantage of each algorithm. When the rate of convergence decreases into a steady level the Gradient Algorithm is then applied so that the MSE will decrease again to a lower value, demonstrating that the combined algorithm obtains a more precise result and improve the performance.
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Dissertation Note
M.S. Pennsylvania State University 2015.
Reproduction Note
Library holds archival microfiches negative and service copy. 1 fiche. (Micrographics International, 2016)
Technical Details
The full text of the dissertation is available as an Adobe Acrobat .pdf file ; Adobe Acrobat Reader required to view the file.
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