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A new method for model order reduction with eigenvalue preservation is presented in this paper. The new technique is formulated based on the system state matrix transformation which preserves the system eigenvalues and is accomplished using an artificial neural network training. A linear matrix inequality (LMI) numerical algorithm technique is used to obtain the complete system transformation. Model order reduction is then obtained utilizing the singular perturbation method. Simulation results show that the LMIbased transformed reduced model order is superior to other reduction methods.

MODEL-ORDER REDUCTION OF SINGULARLY PERTURBED SYSTEMS BASED ON ARTIFICIAL NEURAL ESTIMATION AND LMI-BASED TRANSFORMATION