Goal of the proposed research is the development of new efficient methods for the identification of the Input-output (i/o) behavior of nonlinear dynamical systems. The efficiency in terms of computational complexity should be achieved by exploiting the structural constraints of the nonlinear dynamical system. An accurate description of the i/o behavior of nonlinear dynamical systems such as nonlinear circuits gains in relevance for research and industrial applications. The linearization of nonlinear systems through their inverse is one example of an important area of application. To realize such applications it is necessary to be able to map the i/o behavior of the nonlinear system to a low dimensional representation. All known methods, such as Volterra series, suffer from the problem of exploding computational complexity with the required accuracy of the system description. One reason for this increase is that important structural constraints of the nonlinear system are not accounted for. In die proposed research these structural information such as dimension of die state space, poles of the linearized dynamics, should be used to circumvent.such an increase in computational complexity. In collaboration with Professor Chua and his research group, these structural information should be incorporated into a new general methodology for the identification of nonlinear systems.