Memristive systems are nonlinear resistors with memory. Most of them are realized as resistive switching devices in nanotechnology. One example, with appropriate properties especially in neuromorphic applications, is the double barrier memristive device (DBMD). A continuous resistance range makes the DBMD suitable for replacing the synapses in neuromorphic circuits. Structural and functional descriptions based on physical insights can help to obtain a parametric concentrated model of the device for both reproducible investigations as well as emulations. Achieving physically meaningful values for model parameters in order to fit the measured data is not trivial. We propose a parameter identification method based on an optimization problem. Because of very fast and efficient algorithms, the wave digital method has been utilized in the objective function. As an example, a reduced model of the DBMD with optimized parameters for fitting the measured data is shown.