Abstract
The optimality of the canonical variate analysis (CVA) method is demonstrated for moderately large sample sizes (1,000 to 8,000) by a multivariable simulation example involving feedback, colored input excitation, and colored disturbances. The system has 2 inputs, 2 outputs, 6 states and involves the estimation of 43 parameters including a stochastic model of the noise disturbances. No prior information about the model state order is used in system identification. Monte Carlo simulations demonstrate that the parameter estimation error is within the lower bound theory predicted by the large sample maximium likelihood theory, that the true state order is identified, and that the CVA state estimates are within the expected error of the Kalman filter state estimates of the true system.
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