Multiparameter self-optimizing systems using correlation techniques

A class of self-optimizing systems which continually alter their parameters to reduce a mean-square performance criterion is described. The change in each parameter is determined from an error gradient in parameter space computed by cross-correlation methods which are independent of signal spectra and require no test signal or parameter perturbation. Applications of this technique to both open-loop and closed-loop systems are included and it is shown that a combination of such self-optimizing systems is a possible solution to the adaptive control problem. Computer simulation results are included to demonstrate the practicality of the proposed systems.