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This paper presents a new modification of the multiple integration method [1, 2, 3] for continuous nonlinear SISO system identification from measured input - output data. The model structure is changed compared with [1]. This change enables more sophisticated systems to be identified. The resulting MATLAB program is available in [4]. As was stated in [1], there is no need to reach a steady state of the identified system. The algorithm also automatically filters the measured data with respect to low frequency drifts and offsets, and offers the user a potent tool for selecting the frequency range of validity of the obtained model.

Nonlinear Continuous System Identification by Means of Multiple Integration II