Abstract
This paper describes some methods for identification of linear systems using the input and the output information under operating conditions. The linear system is expressed by an impulse response whose sampled values are to be estimated successively as unknown parameters.
These methods are as follows: The same input signal is applied both to the unknown system and to its mathematical model, and the parameters of the model are adjusted succesively so as to minimize the performance criterion, which is the squared difference of the outputs of both systems. And the dynamics of the unknown system is recognized from the model which has approximated sufficiently to the unknown system.
As methods for parameter adjustment, this paper proposes three modified gradient methods which use two-valued signals to yield the parameter adjustment signals in order to reduce the quantity of calculation and simplify the structure in making the hardware, that is; (1) a method quantizing the input signal, (2) a method quantizing the output error signal, (3) a method quantizing both the input signal and the output error signal.
For each proposed method the authors derive the convergence condition of the estimated parameters and the optimum condition for quick convergence and show the results of the digital simulation, which support the described theory.
