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111
Perspectives on system identification
 In Plenary talk at the proceedings of the 17th IFAC World Congress, Seoul, South Korea
, 2008
"... System identification is the art and science of building mathematical models of dynamic systems from observed inputoutput data. It can be seen as the interface between the real world of applications and the mathematical world of control theory and model abstractions. As such, it is an ubiquitous ne ..."
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Cited by 77 (2 self)
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System identification is the art and science of building mathematical models of dynamic systems from observed inputoutput data. It can be seen as the interface between the real world of applications and the mathematical world of control theory and model abstractions. As such, it is an ubiquitous necessity for successful applications. System identification is a very large topic, with different techniques that depend on the character of the models to be estimated: linear, nonlinear, hybrid, nonparametric etc. At the same time, the area can be characterized by a small number of leading principles, e.g. to look for sustainable descriptions by proper decisions in the triangle of model complexity, information contents in the data, and effective validation. The area has many facets and there are many approaches and methods. A tutorial or a survey in a few pages is not quite possible. Instead, this presentation aims at giving an overview of the “science ” side, i.e. basic principles and results and at pointing to open problem areas in the practical, “art”, side of how to approach and solve a real problem. 1.
Nonlinear BlackBox Models in System Identification: Mathematical Foundations
, 1995
"... In this paper we discuss several aspects of the mathematical foundations of nonlinear blackbox identification problem. As we shall see that the quality of the identification procedure is always a result of a certain tradeoff between the expressive power of the model we try to identify (the larger ..."
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Cited by 29 (5 self)
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In this paper we discuss several aspects of the mathematical foundations of nonlinear blackbox identification problem. As we shall see that the quality of the identification procedure is always a result of a certain tradeoff between the expressive power of the model we try to identify (the larger is the number of parameters used to describe the model, more flexible would be the approximation), and the stochastic error (which is proportional to the number of parameters). A consequence of this tradeoff is a simple fact that good approximation technique can be a basis of good identification algorithm. From this point of view we consider different approximation methods, and pay special attention to spatially adaptive approximants. We introduce wavelet and "neuron" approximations and show that they are spatially adaptive. Then we apply the acquired approximation experience to estimation problems. Finally, we consider some implications of these theoretic developments for the practically...
Identification of piecewise affine systems via mixedinteger programming
 Automatica
, 2004
"... This paper addresses the problem of identification of hybrid dynamical systems, by focusing the attention on hinging hyperplanes (HHARX) and Wiener piecewise affine (WPWARX) autoregressive exogenous models. In particular, we provide algorithms based on mixedinteger linear or quadratic programming ..."
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Cited by 22 (4 self)
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This paper addresses the problem of identification of hybrid dynamical systems, by focusing the attention on hinging hyperplanes (HHARX) and Wiener piecewise affine (WPWARX) autoregressive exogenous models. In particular, we provide algorithms based on mixedinteger linear or quadratic programming which are guaranteed to converge to a global optimum. For the special case where switches occur only seldom in the estimation data, we also suggest a way of trading off between optimality and complexity by using a change detection approach. 1
OnBoard Component Fault Detection and Isolation Using the Statistical Local Approach
, 1997
"... We describe both the key principles and real application examples of a unified theory which allows us to perform the onboard incipient fault detection and isolation tasks involved in monitoring for conditionbased maintenance. We stress that, when designing detection algorithms, the main conceptual ..."
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Cited by 22 (6 self)
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We describe both the key principles and real application examples of a unified theory which allows us to perform the onboard incipient fault detection and isolation tasks involved in monitoring for conditionbased maintenance. We stress that, when designing detection algorithms, the main conceptual task is to select a convenient estimating function. ml, ls, iv and subspace identification methods are addressed in this perspective.
A Taxonomy for Spatiotemporal Connectionist Networks Revisited: The Unsupervised Case
 Neural Computation
, 2003
"... Spatiotemporal connectionist networks (STCN's) comprise an important class of neural models that can deal with patterns distributed both in time and space. In this paper, we widen the application domain of the taxonomy for supervised STCN's recently proposed by Kremer (2001) to the unsupervised case ..."
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Cited by 21 (1 self)
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Spatiotemporal connectionist networks (STCN's) comprise an important class of neural models that can deal with patterns distributed both in time and space. In this paper, we widen the application domain of the taxonomy for supervised STCN's recently proposed by Kremer (2001) to the unsupervised case. This is possible through a reinterpretation of the state vector as a vector of latent (hidden) variables, as proposed by Meinicke (2000). The goal of this generalized taxonomy is then to provide a nonlinear generative framework for describing unsupervised spatiotemporal networks, making it easier to compare and contrast their representational and operational characteristics. Computational properties, representational issues and learning are also discussed and a number of references to the relevant source publications are provided. It is argued that the proposed approach is simple and more powerful than the previous attempts, from a descriptive and predictive viewpoint. We also discuss the relation of this taxonomy with automata theory and state space modeling, and suggest directions for further work.
JustinTime Models with Applications to Dynamical Systems
 Dept. of EE, LinkOping University. S581 83 LinkOping
, 1997
"... System identification deals with the problem of estimating models of dynamical systems given observations from the systems. In this thesis we focus on the nonlinear modeling problem, and, in particular, on the situation that occurs when a very large amount of data is available. Traditional treatmen ..."
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Cited by 21 (3 self)
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System identification deals with the problem of estimating models of dynamical systems given observations from the systems. In this thesis we focus on the nonlinear modeling problem, and, in particular, on the situation that occurs when a very large amount of data is available. Traditional treatments of the estimation problem in statistics and system identification have mainly focused on global modeling approaches, i.e., the model has been optimized using the entire data set. However, when the number of samples becomes large, this approach becomes less attractive mainly because of the computational complexity. We instead assume that all observations are stored in a database, and that models are built dynamically as the actual need arises. When a model is really needed in a neighborhood around an operating point, a subset of the data closest to the operating point is retrieved from the database, and a local modeling operation is performed on that subset. For this concept, the name Jus...
managed challenge’ alleviates disengagement in coevolutionary system identification
 GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation
, 2005
"... In previous papers we have described a coevolutionary algorithm (EEA), the estimationexploration algorithm, that infers the hidden inner structure of systems using minimal testing. In this paper we introduce the concept of ‘managed challenge ’ to alleviate the problem of disengagement in this and ..."
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Cited by 19 (5 self)
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In previous papers we have described a coevolutionary algorithm (EEA), the estimationexploration algorithm, that infers the hidden inner structure of systems using minimal testing. In this paper we introduce the concept of ‘managed challenge ’ to alleviate the problem of disengagement in this and other coevolutionary algorithms. A known problem in coevolutionary dynamics occurs when one population systematically outperforms the other, resulting in a loss of selection pressure for both populations. In system identification (which deals with determining the inner structure of a system using only input/output data), multiple trials (a test that causes the system to produce some output) on the system to be identified must be performed. When such trials are costly, this disengagement results in wasted data that is not utilized by the evolutionary process. Here we propose that data from futile interactions should be stored during disengagement and automatically reintroduced later, when the population reengages: we refer to this as the test bank. We demonstrate that the advantage of the test bank is twofold: it allows for the discovery of more accurate models, and it reduces the amount of required training data for both parametric identification – parameterizing inner structure – and symbolic identification – approximating inner structure using symbolic equations – of nonlinear systems.
Online prediction of time series data with kernels
 IEEE TRANS. SIGNAL PROCESSING
, 2009
"... Kernelbased algorithms have been a topic of considerable interest in the machine learning community over the last ten years. Their attractiveness resides in their elegant treatment of nonlinear problems. They have been successfully applied to pattern recognition, regression and density estimation. ..."
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Cited by 16 (13 self)
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Kernelbased algorithms have been a topic of considerable interest in the machine learning community over the last ten years. Their attractiveness resides in their elegant treatment of nonlinear problems. They have been successfully applied to pattern recognition, regression and density estimation. A common characteristic of kernelbased methods is that they deal with kernel expansions whose number of terms equals the number of input data, making them unsuitable for online applications. Recently, several solutions have been proposed to circumvent this computational burden in time series prediction problems. Nevertheless, most of them require excessively elaborate and costly operations. In this paper, we investigate a new model reduction criterion that makes computationally demanding sparsification procedures unnecessary. The increase in the number of variables is controlled by the coherence parameter, a fundamental quantity that characterizes the behavior of dictionaries in sparse approximation problems. We incorporate the coherence criterion into a new kernelbased affine projection algorithm for time series prediction. We also derive the kernelbased normalized LMS algorithm as a particular case. Finally, experiments are conducted to compare our approach to existing methods.
Forecasting Electricity Consumption Using Nonlinear Projection and SelfOrganizing Maps
, 2002
"... A generalpurpose useful parameteri tia serie forecasti# i the regressorsire correspondiS to themijhTq number ofvariSPhj necessary to forecast the future values of thetiS seriTh If the models are nonlihKjR thechoiK ofthi regressor becomes very diy #cult. We present aquasijRIPE#Sji methodusio a nonl ..."
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Cited by 13 (6 self)
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A generalpurpose useful parameteri tia serie forecasti# i the regressorsire correspondiS to themijhTq number ofvariSPhj necessary to forecast the future values of thetiS seriTh If the models are nonlihKjR thechoiK ofthi regressor becomes very diy #cult. We present aquasijRIPE#Sji methodusio a nonliIjR projectiP namedcurvi66hIj component analysi tobuiK thi regressor. The sij ofthi regressorwir bedetermiRI by the estihjRIq of theiejhPSII dijhPSII of anoversiRI regressor.Thi methodwih be appliq toelectri consumpti# of Polandusin systemati crossvalijRI#TT ThenonliqSE model used for the prediqEjR i a Kohonen map(selforganiTPj map). c 2002Publi6#S byElsevij Sciij B.V. Keywords:Ti4 seri0 prediWjhIK NonliWj projectiIK CurvitiIK component analysit SelforganiPIS map; ElectriqjR consumptiR 1. I41pz122 TiI serii forecastii i a great challengei many #elds. In #nance, one forecasts stock exchange courses orijII6P of stock markets; dataprocessij specisijR forecast the #ow ofihK#TEjREh onthei networks; producers ofelectriREh forecast the load of thefollowiE day. The commonpoio tothei problemsi Correspondi author.
Compact Application Signatures for Parallel and Distributed Scientific Codes (Extended Abstract)
"... Understanding the dynamic behavior of parallel programs is key to developing efficient system software and runtime environments; this is even more true on emerging computational Grids where resource availability and performance can change in unpredictable ways. Event tracing provides details on beha ..."
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Cited by 12 (1 self)
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Understanding the dynamic behavior of parallel programs is key to developing efficient system software and runtime environments; this is even more true on emerging computational Grids where resource availability and performance can change in unpredictable ways. Event tracing provides details on behavioral dynamics, albeit often at great cost. We describe an intermediate approach, based on curve fitting, that retains many of the advantages of event tracing but with lower overhead. These compact "application signatures" summarize the timevarying resource needs of scientific codes from historical trace data.