1. Introduction. applications of analog systems despite their unification with digital parts still remains important in various technical domains, such as military, acoustic or radio frequency (RF). The continuously growing the number of elements makes their testability (i.e. the ability to distinguish between particular fault sources) difficult to obtain. In case of high frequency and data acquisition systems, conducting the diagnostics of analog and digital parts separately is crucial in achieving satisfying work evaluation results. The testability of digital circuits have already been well defined, there are no such procedures prepared for the analog and mixed systems . Identification of such systems is a key to decrease the production costs of modern electronics . The analog systems, diagnostics is complicated by the tolerances of elements or noise (which must be treated during the signal processing operations prior to the fault detection or identification). The testability of mixed systems is included in the IEEE 1149.4 norm . For analog systems, no such standards exist. The Artificial Intelligence (AI) approaches have been extensively used to monitor the state of analog systems during the last twenty years. Currently, sophisticated computer systems are able to perform fault detection, location and identification in the real time. The advanced concepts of the AI include the classifier fusion  or the combination of supervised and unsupervised learning systems . Contemporary approaches are heuristic and their parameters must be optimized to maximize the accuracy. Therefore one of the most pressing issues in the fault detection and location domain is the investigation and applications of optimization methods. The paper presents the overview of optimization algorithms and their applications to particular problems. The most widely used approaches are briefly described and their diagnostic implementations consi[...]
Wyniki 1-1 spośród 1 dla zapytania: authorDesc:"Adrian Bilski"