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

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.

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