A current-mode chaos circuit with return map approximation using supervised learning

A new current-mode analog chaos circuit is proposed. The proposed circuit is composed of an operation block, a parameter block, and a delay block. The return maps of this circuit are generated in the neuro-fuzzy based operation block. These maps are determined by supervised learning. For the proposed circuit, the dynamics of the learning and the state of the chaos are analyzed by computer simulations. The theoretical analysis is carried out to predict the bifurcation diagrams under the existence of the nonidealities in the piecewise linear approximated return maps. The simulation results showed that the return maps can be realized with the precision of the order of several percent and that different kinds of bifurcation modes can be generated easily