A current-mode sampled-data chaos circuit with nonlinear mapping function learning

A new current-mode sampled-data chaos circuit is proposed. The proposed circuit is composed of an operation block, a parameter block, and a delay block. The nonlinear mapping functions of this circuit are generated in the neuro-fuzzy based operation block. And these Functions are determined by supervised learning. For the proposed circuit, the dynamics df the learning and the state of the chaos are analyzed by computer simulations. The design conditions concerning the bifurcation diagram and the nonlinear mapping function are presented to clarify the chaos generating conditions and the effect of nonidealities of the proposed circuit. The simulation results showed that the nonlinear mapping functions can be realized with the precision of the order of several percent and that different kinds of bifurcation modes can be generated easily.