کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
382669 | 660778 | 2013 | 11 صفحه PDF | دانلود رایگان |

In view of the great potential in parallel processing and ready implementation via hardware, neural networks are now often employed to solve online nonlinear matrix equation problems. Recently, a novel class of neural networks, termed Zhang neural network (ZNN), has been formally proposed by Zhang et al. for solving online time-varying problems. Such a neural-dynamic system is elegantly designed by defining an indefinite matrix-valued error-monitoring function, which is called Zhang function (ZF). The dynamical system is then cast in the form of a first-order differential equation by using matrix notation. In this paper, different indefinite ZFs, which lead to different ZNN models, are proposed and developed as the error-monitoring functions for time-varying matrix square roots finding. Towards the final purpose of field programmable gate array (FPGA) and application-specific integrated circuit (ASIC) realization, the MATLAB Simulink modeling and verifications of such ZNN models are further investigated for online solution of time-varying matrix square roots. Both theoretical analysis and modeling results substantiate the efficacy of the proposed ZNN models for time-varying matrix square roots finding.
► Different ZNN models are proposed for time-varying matrix square roots finding.
► We present the convergence results synthesized by the ZNN models.
► MATLAB Simulink modeling techniques are exploited for verifying the ZNN models.
► Modeling results are presented and the efficacy of the ZNN models is verified.
Journal: Expert Systems with Applications - Volume 40, Issue 11, 1 September 2013, Pages 4393–4403