Abstract
Along with neural dynamics (based on analog solvers) widely arising in scientific computation and optimization fields in recent decades which attracts extensive interest and investigation of researchers, a special type of neural dynamics, called Zhang dynamics (ZD), has been formally proposed by Zhang et al. for real-time solution of time-varying problems. By following Zhang et al.’s neural-dynamics design method, the ZD model, which is based on an indefinite Zhang function (ZF), can guarantee the exponential convergence performance for time-varying problems solving. In this chapter, for time-varying reciprocal finding, we propose, generalize, develop, and investigate different indefinite ZFs as the error-monitoring functions, which can lead to different ZD models. In addition, for the goal of developing the floating-point processors or coprocessors for the future generation of computers, the MATLAB Simulink modeling and simulative verifications of such different ZD models are presented. The modeling results further substantiate the efficacy of the proposed ZD models for time-varying reciprocal finding.
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