Hybrid knowledge-based system/multilayer perceptron approach for the post-assembly tuning of electronic filters

A generic approach to the development of a hybrid knowledge-based system (KBS)-multilayer perceptron (MLP) system for the post-assembly tuning of filters is described. This KBS uses rules derived using the Iterative Dichotomiser 3 to advise upon which filter component to adjust and in which direction. The problem is divided into a number of search spaces corresponding to three levels of decision, and numerical attribute values are represented by numerical ranges with logical names. How far to turn is determined by a set of MLPs, trained on the amplitude responses of a typical filter. It is established that the task requires a hybrid system and cannot be satisfactorily performed by either a KBS or MLP alone. When applied to the amplitude response tuning of four-pole asymmetric bandpass crystal filters three adjustments are typically required, on average, to tune the stopbands of 79% of those tested. The lengthy development times needed per filter type of 1-2 weeks are compensated for as the task is within the capabilities of a trained operator