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Soft Computing

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01.11.2013 | Focus

Z-type and G-type models for time-varying inverse square root (TVISR) solving

verfasst von: Yunong Zhang, Zhen Li, Dongsheng Guo, Weibing Li, Pei Chen

Erschienen in: Soft Computing | Ausgabe 11/2013

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Abstract

A class of neural dynamics, called Zhang dynamics (ZD), has been proposed to solve online various time-varying problems. In this paper, different Z-type (Zhang type) models based on different Zhang functions (ZFs) are proposed, investigated and simulated for solving the time-varying inverse square root (or in short, TVISR) problem. Then, for the same problem-solving task, different G-type (gradient type) models based on different energy functions (EFs) are developed and investigated as well. Moreover, the convergence analyses of Z-type and G-type models are studied in-depth for the completeness of this paper. Besides, for possible circuit and/or computer realization, Matlab Simulink modeling of Z-type and G-type models is illustrated. Through illustrative examples, the efficacy and superiority of the proposed Z-type and G-type models for TVISR problem solving are verified and substantiated.

Vorheriger Artikel On the global existence of dissipative solutions for the modified coupled Camassa–Holm system

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Titel: Z-type and G-type models for time-varying inverse square root (TVISR) solving
verfasst von: Yunong Zhang
Zhen Li
Dongsheng Guo
Weibing Li
Pei Chen
Publikationsdatum: 01.11.2013
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 11/2013
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-013-1124-5

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