A learning method for system identification

Abstract

A method for system identification is proposed which is based on the error-correcting training procedure in learning machines, and is referred to as "learning identification." This learning identification is nondisturbing, is applicable to cases where the input signal is random and nonstationary, and can be completed within a short time, so that it may be used to identify linear quasi-time-invariant systems in which some parameters vary slowly in comparison with the time required for identification. This merit also makes it possible to eliminate noise disturbances by means of the moving average method. Computer simulation of the learning identification was carried out and the times required for identification were obtained for various cases. Some modifications of the learning identification were also investigated together with their computer simulations.