First-order tracking properties of weighted least squaresestimators

The first-order tracking properties (evolution of the mean tracking error) of weighted-least-squares (WLS) estimators applied to nonstationary system identification are investigated. The expected path of the parameter estimates is evaluated. The concept of the impulse response associated with the WLS estimator is introduced     

Nonasymptotic results for finite-memory WLS filters

A nonasymptotic analysis of properties of weighted least squares (WLS) adaptive filters used for identification of time-varying systems is presented. It is shown that the problem of mean-square boundedness of WLS estimates is closely related to the problem of invertibility-in the mean sense-of the corresponding regression matrix. Necessary and sufficient conditions are discussed for such invertibility to hold. Based on that, a number of results are derived paralleling those already obtained for least mean-square (LMS) filters, and the problem of statistical robustness of the WLS estimator is briefly discussed     

On tracking characteristics of weighted least squares estimatorsapplied to nonstationary system identification

The parameter-tracking abilities of weighted-least-squares (WLS) estimators are characterized in terms of a suitably defined variability matrix and associated frequency characteristics. Using these concepts, the conflict arising between the estimation variance and estimation bandwidth is easily explained and quantified     

Identification/prediction algorithms for armax models with relaxed positive real conditions

Extended least squares (ELS) algorithms are proposed for ARMAX model identification with the objective of avoiding the positive real condition associated with standard equation error and output error algorithms. This is achieved by an overparametrization at the cost of additional richness requirements on excitation signals, but without introducing ill-conditioning or infinite-dimensional calculations as in earlier methods. Results for the case of D-step-ahead prediction ELS algorithms for ARMAX models are also explored in the paper. Some simulation studies are included to assess the relative performance characteristics of the proposed algorithms, and the nature of the relaxed positive real condition for different degrees of overparametrization N is investigated in detail for second-order noise models.     

High-performance and cost-effective membrane electrode assemblies for advanced proton exchange membrane water electrolyzers: Long-term durability assessment

Improved activity and durability performance of a two-cell (86 cm²) proton exchange membrane water electrolyzer (PEMWE) stack is reported for the first time. Both membrane electrode assemblies (MEAs) contain one order of magnitude lower platinum group metal (PGM) loadings compared to the state-of-the-art PEMWEs and incorporate novel Pt recombination layers. The high-performance and cost-effective MEAs are fabricated by the unique reactive spray deposition technology (RSDT). This advanced methodology allows for one-step fabrication of MEAs and ensures precise control and distribution of the catalyst composition and loading. The RSDT-fabricated MEAs contain only 0.2 and 0.3 mg_PGM cm^?2 loading in the cathode and anode electrodes, respectively, and demonstrate excellent activity and durability for over 3000 h of operation at industrially-relevant operating conditions without showing significant loss in performance. This novel work shows that a significant cost reduction for PEMWEs is achieved while maintaining excellent durability, high catalysts activities, and low hydrogen cross-over.     

Self-Optimizing Adaptive Vibration Controller

This paper presents a new approach to rejection of sinusoidal disturbances acting at the output of a discrete-time linear stable plant with unknown dynamics. It is assumed that the frequency of the sinusoidal disturbance is known, and that the output signal is contaminated with wideband measurement noise. The proposed controller, called SONIC (self-optimizing narrowband interference canceller), combines the coefficient fixing technique, used to "robustify" self-tuning minimum-variance regulators, with automatic adaptation gain tuning. Both theoretical analysis and computer simulations confirm that, under Gaussian assumptions, the closed-loop system converges (locally) in mean to the optimal solution.     

Generalized Adaptive Notch Smoothers for Real-Valued Signals and Systems

Systems with quasi-periodically varying coefficients can be tracked using the algorithms known as generalized adaptive notch filters (GANFs). GANF algorithms can be considered an extension, to the system case, of classical adaptive notch filters (ANFs). We show that estimation accuracy of the existing algorithms, as well as their robustness to the choice of design parameters, can be considerably improved by means of compensating estimation delay effects which occur in the amplitude tracking and frequency tracking loops of GANF/ANF filters. Apart from the increased computational burden, the price for the achieved improvements is paid in terms of a decision delay-the proposed generalized adaptive notch smoothing (GANS) algorithms must be run on delayed input/output data sequences. Since such delay is acceptable in many signal processing and system identification applications, the proposed solution seems to be an attractive alternative to the currently available trackers.     

On the localized estimators and generalized Akaike's criteria

The problem of nonstationary system modeling is considered and the local modeling approach is proposed for its solution. Initially, the concept of localized maximum likelihood estimators is introduced and applied to approximation of time-varying stochastic systems. Two types of such estimators, the first based on the concept of weighting and the second based on the concept of data windowing, are proposed and discussed in some detail in the case of autoregressive systems, Next, the problem of the proper choice of the model structure is considered. It is shown that the criterion for model order selection proposed by Akaike for the case of maximum likelihood estimation (information criterion) can be extended to the case of localized estimators.     

Fast recursive basis function estimators for identification oftime-varying processes

When system parameters vary rapidly with time, the weighted least squares filters are not capable of following the changes satisfactorily; some more elaborate estimation schemes, based on the method of basis functions, have to be used instead. The basis function estimators have increased tracking capabilities but are computationally very demanding. The paper introduces a new class of adaptive filters, based on the concept of postfiltering, which have improved parameter tracking capabilities that are typical of the basis function algorithms but, at the same time, have pretty low computational requirements, which is typical of the weighted least squares algorithms