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Kinetics of carrier recombination in amorphous solids
PublikacjaPrzeanalizowano zależność pomiędzy teoriami Simmonsa-Taylora oraz Rose´a, opisującą niestacjonarne procesy rekombinacji nośników nadmiernych, ich pułapkowania i uwalniania ze stanów zlokalizowanych. Równania Simmonsa-Taylora wyrażono w warunkach gęstości nośnika w stanach zlokalizowanych. Pokazano, że w pewnych przypadkach równania redukują się do równań Rose´a, odnoszących się do bimolekularnej lub monomolekularnej rekombinacji.
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On noncausal weighted least squares identification of nonstationary stochastic systems
PublikacjaIn this paper, we consider the problem of noncausal identification of nonstationary, linear stochastic systems, i.e., identification based on prerecorded input/output data. We show how several competing weighted (windowed) least squares parameter smoothers, differing in memory settings, can be combined together to yield a better and more reliable smoothing algorithm. The resulting parallel estimation scheme automatically adjusts...
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On the lower smoothing bound in identification of time-varying systems
PublikacjaIn certain applications of nonstationary system identification the model-based decisions can be postponed, i.e. executed with a delay. This allows one to incorporate in the identification process not only the currently available information, but also a number of ''future'' data points. The resulting estimation schemes, which involve smoothing, are not causal. Assuming that the infinite observation history is available, the paper...
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Self-optimizing generalized adaptive notch filters - comparison of three optimization strategies
PublikacjaThe paper provides comparison of three different approaches to on-line tuning of generalized adaptive notch filters (GANFs) the algorithms used for identification/tracking of quasi-periodically varying dynamic systems. Tuning is needed to adjust adaptation gains, which control tracking performance of ANF algorithms, to the unknown and/or time time-varying rate of system nonstationarity. Two out ofthree compared approaches are classical...
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On the concept of estimation memory in adaptive filtering.
PublikacjaArtykuł przedstawia i omawia pojęcie pamięci estymacji, pozwalające na obiektywne porównanie właściwości śledzących różnych algorytmów adaptacyjnych filtracji.
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Fast recursive basis function estimators for identification of time-varying processes
PublikacjaW pracy wprowadzono nową kategorię filtrów adaptacyjnych opartych na metodzie funkcji bazowych i wykorzystujących koncepcję postfiltracji. Proponowane algorytmy pozwalają połączyć niską złożoność obliczeniową i dobre właściwości śledzące.
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On tracking properties of real-valued generalized adaptive notch filters
PublikacjaGeneralized adaptive notch filters (GANFs) are used for identification/tracking of quasi-periodically varying dynamic systems and can be considered an extension, to the system case, of classical adaptive notch filters. The paper presents results of local performance analysis of a real-valued GANF algorithm, i.e., algorithm designed to track parameters of a real-valued system. This is an extension of the previous work which focused...
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A simple way of increasing estimation accuracy of generalized adaptive notch filters
PublikacjaGeneralized adaptive notch filters are used for identification/tracking of quasi-periodically varying dynamic systems and can be considered an extension, to the system case, of classical adaptive notch filters. It is shown that frequency biases, which arisein generalized adaptive notch filtering algorithms, can be significantly reduced by incorporating in the adaptive loop an appropriately chosen decision delay. The resulting performance...
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Optimal and suboptimal algorithms for identification of time-varying systems with randomly drifting parameters
PublikacjaNoncausal estimation algorithms, which involve smoothing, can be used for off-line identification of nonstationary systems. Since smoothingis based on both past and future data, it offers increased accuracy compared to causal (tracking) estimation schemes, incorporating past data only. It is shown that efficient smoothing variants of the popular exponentially weighted least squares and Kalman filter-based parameter trackers can...