Application of a recursive subspace identification method to derive a state space model for a synchronous machine is described in this paper. Simulation studies show the effectiveness of such an algorithm to identify ...
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Application of a recursive subspace identification method to derive a state space model for a synchronous machine is described in this paper. Simulation studies show the effectiveness of such an algorithm to identify on-line a synchronous machine model over a wide range of operating conditions and disturbances. The model provides a foundation for further study on a MIMO adaptive power system stabilizer.
The problem of the recursive identification of MIMO state space models in the framework of subspace methods is considered in this article. Two new algorithms, based on a recursive formulation of the MOESP identificati...
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The problem of the recursive identification of MIMO state space models in the framework of subspace methods is considered in this article. Two new algorithms, based on a recursive formulation of the MOESP identification class, are more precisely developed. The specific feature of these methods is that they share a single algorithm to recursively estimate a basis of the observability matrix. Two propagator based (Murder and Delisle, 1991) criteria are introduced. A sequential RLS algorithm is proposed to equally minimise these cost functions. The benefits of these new techniques in comparison with EIVPAST and UDPAST (Lovera et al. , 2000) are emphasized with a simulation example.
Let Un denote the number of partitions of the natural number n, all of whose parts bi to {P-n,}- In this note, we present a recursive algorithm for computing Un. The techn used here are also applicable to other second...
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Let Un denote the number of partitions of the natural number n, all of whose parts bi to {P-n,}- In this note, we present a recursive algorithm for computing Un. The techn used here are also applicable to other second order linear recurrences that have the pro] of being super-increasing, that is, where each term exceeds the sum of all its predecessoi [1], the second author solved the corresponding problem for the Fibonacci sequence.
A class of random recursive sequences (Y-n) with slowly varying variances as arising for parameters of random trees or recursive algorithms X leads after normalizations to degenerate limit equations of the form X =(L)...
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A class of random recursive sequences (Y-n) with slowly varying variances as arising for parameters of random trees or recursive algorithms X leads after normalizations to degenerate limit equations of the form X =(L) X. For nondegenerate limit equations the contraction method is a main tool to establish convergence of the scaled sequence to the "unique" solution of the limit equation. In this paper we develop an extension of the contraction method which allows us to derive limit theorems for parameters of algorithms and data structures with degenerate limit equation. In particular, we establish some new tools and a general convergence scheme, which transfers information on mean and variance into a central limit law (with normal limit). We also obtain a convergence rate result. For the proof we use selfdecomposability properties of the limit normal distribution which allow us to mimic the recursive sequence by an accompanying sequence in normal variables.
In this paper we propose a new recursive algorithm for the regulation via state feedback of systems in so-called parametric feedback form. The algorithm resembles the well-known adaptive backstepping approach, but dif...
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In this paper we propose a new recursive algorithm for the regulation via state feedback of systems in so-called parametric feedback form. The algorithm resembles the well-known adaptive backstepping approach, but differs in the way the parameter estimator is constructed and in the way the nonlinear gains are assigned. A comparative case study is presented, highlighting the advantages of the proposed methodology.
In this paper, we investigate the behavior of certain types of mixed time scale adaptive algorithms. These systems comprise a "fast" or quickly changing algorithm mutually coupled to a "slow" or sl...
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In this paper, we investigate the behavior of certain types of mixed time scale adaptive algorithms. These systems comprise a "fast" or quickly changing algorithm mutually coupled to a "slow" or slowly changing algorithm, They arise naturally in a variety of adaptive environments such as in IIR system identification, the training of recurrent neural networks, decision feedback equalization, and others. [These algorithms (despite their title) should not be confused with the mixed time scales of wavelet transforms or other algorithms associated with multiresolution signal processing.] We give conditions for when the system can be analyzed from the framework of a simpler "frozen state" system, This analysis extends some of the previous work of Solo and his coworkers.
In this paper we give an introduction to the analysis of algorithms by the contraction method. By means of this method several interesting classes of recursions can be analyzed as particular cases of our general frame...
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In this paper we give an introduction to the analysis of algorithms by the contraction method. By means of this method several interesting classes of recursions can be analyzed as particular cases of our general framework. We introduce the main steps of this technique which is based on contraction properties of the algorithm with respect to suitable probability metrics. Typically the limiting distribution is characterized as a fixed point of a limiting operator on the class of probability distributions. We explain this method in the context of several "divide and conquer" algorithms. In the second part of the paper we introduce a new quite general model for branching dynamical systems and explain that the contraction method can be applied in this model. This model includes many classical examples of random trees and gives a general frame for further applications.
The parameter-uncertainty-interval-estimation (PIE) problem is transferred into several dual-linear-programming (DLP) problems, and a recursive DLP algorithm is proposed and applied in PIE. This recursive algorithm is...
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The parameter-uncertainty-interval-estimation (PIE) problem is transferred into several dual-linear-programming (DLP) problems, and a recursive DLP algorithm is proposed and applied in PIE. This recursive algorithm is based on a modified dual-simplex method that is specifically developed for solving the DLP problems derived from the PIE problem. Because the scale of the DLP problems is considerably smaller than that of the primal linear-programming problems for solving the PIE problem, the recursive DLP algorithm is computationally efficient and suitable for online-engineering applications. Simulations show the effectiveness of the proposed recursive algorithm.
In this paper, a new improved recursive Newton-type algorithm suitable for various measurement applications in electric power systems is presented. It is used for the power system frequency and spectra estimation. The...
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In this paper, a new improved recursive Newton-type algorithm suitable for various measurement applications in electric power systems is presented. It is used for the power system frequency and spectra estimation. The recursive algorithm form is improved with a strategy of sequential tuning of the forgetting factor. By this, the algorithm convergence and accuracy are significantly improved. To show the main algorithm features, the results of computer simulation, laboratory testing (load rejection and unsuccessful symchronization tests) and field data processing are given.
We consider the implications of streaming data for data analysis and data mining. Streaming data are becoming widely available from a variety of sources. In our case we consider the implications arising from Internet ...
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We consider the implications of streaming data for data analysis and data mining. Streaming data are becoming widely available from a variety of sources. In our case we consider the implications arising from Internet traffic data. By implication, streaming data are unlikely to be time homogeneous so that standard statistical and data mining procedures do not necessarily apply. Because it is essentially impossible to store streaming data, we consider recursive algorithms, algorithms which are adaptive and discount the past and also algorithms that create finite pseudo-samples. We also suggest some evolutionary graphics procedures that are suitable for streaming data. We begin our discussion with a discussion of Internet traffic in order to give the reader some sense of the time and data scale and visual resolution needed for such problems.
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