Identifying odd/even-order binary kernel slices for a nonlinear system using inverse repeat m-sequences. Hu JY, Yan G, Wang T - Comput Math Methods Med (2015) Bottom Line: The study of various living complex systems by system identification method is important, and the identification of the problem is even more challenging when dealing with a dynamic nonlinear system of discrete time.In this study, we examine the relevant mathematical properties of kernel slices, particularly their shift-and-product property and overlap distortion problem caused by the irregular shifting of the estimated kernel slices in the cross-correlation function between the input m-sequence and the system output.We then derive the properties of the inverse repeat (IR) m-sequence and propose a method of using IR m-sequence as an input to separately estimate odd- and even-order kernel slices to reduce the chance of kernel-slice overlapping. View Article: PubMed Central - PubMed Affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China. ABSTRACTThe study of various living complex systems by system identification method is important, and the identification of the problem is even more challenging when dealing with a dynamic nonlinear system of discrete time. A well-established model based on kernel functions for input of the maximum length sequence (m-sequence) can be used to estimate nonlinear binary kernel slices using cross-correlation method. In this study, we examine the relevant mathematical properties of kernel slices, particularly their shift-and-product property and overlap distortion problem caused by the irregular shifting of the estimated kernel slices in the cross-correlation function between the input m-sequence and the system output. We then derive the properties of the inverse repeat (IR) m-sequence and propose a method of using IR m-sequence as an input to separately estimate odd- and even-order kernel slices to reduce the chance of kernel-slice overlapping. An instance of third-order Wiener nonlinear model is simulated to justify the proposed method. Show MeSH MajorComputer Simulation*Nonlinear Dynamics*MinorAlgorithmsComputational BiologyNormal DistributionSoftwareSystems Biology Related in: MedlinePlus © Copyright Policy Related In: Results  -  Collection License getmorefigures.php?uid=PMC4385604&req=5 .flowplayer { width: px; height: px; } fig4: Odd- and even-order kernel slices separately displayed in two cross-correlation functions for IR m-sequence method. Mentions: The kernel slices estimated for the IR m-sequence input a[n] are shown in Figure 4. The odd- and even-order kernel slices are separated into two traces of the cross-correlation, as indicated in (27). All slices are notably separated without any overlap.

Identifying odd/even-order binary kernel slices for a nonlinear system using inverse repeat m-sequences.

Hu JY, Yan G, Wang T - Comput Math Methods Med (2015)

Related In: Results  -  Collection

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fig4: Odd- and even-order kernel slices separately displayed in two cross-correlation functions for IR m-sequence method.
Mentions: The kernel slices estimated for the IR m-sequence input a[n] are shown in Figure 4. The odd- and even-order kernel slices are separated into two traces of the cross-correlation, as indicated in (27). All slices are notably separated without any overlap.

Bottom Line: The study of various living complex systems by system identification method is important, and the identification of the problem is even more challenging when dealing with a dynamic nonlinear system of discrete time.In this study, we examine the relevant mathematical properties of kernel slices, particularly their shift-and-product property and overlap distortion problem caused by the irregular shifting of the estimated kernel slices in the cross-correlation function between the input m-sequence and the system output.We then derive the properties of the inverse repeat (IR) m-sequence and propose a method of using IR m-sequence as an input to separately estimate odd- and even-order kernel slices to reduce the chance of kernel-slice overlapping.

View Article: PubMed Central - PubMed

Affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China.

ABSTRACT
The study of various living complex systems by system identification method is important, and the identification of the problem is even more challenging when dealing with a dynamic nonlinear system of discrete time. A well-established model based on kernel functions for input of the maximum length sequence (m-sequence) can be used to estimate nonlinear binary kernel slices using cross-correlation method. In this study, we examine the relevant mathematical properties of kernel slices, particularly their shift-and-product property and overlap distortion problem caused by the irregular shifting of the estimated kernel slices in the cross-correlation function between the input m-sequence and the system output. We then derive the properties of the inverse repeat (IR) m-sequence and propose a method of using IR m-sequence as an input to separately estimate odd- and even-order kernel slices to reduce the chance of kernel-slice overlapping. An instance of third-order Wiener nonlinear model is simulated to justify the proposed method.

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Related in: MedlinePlus