Switched and PieceWise Nonlinear Hybrid System Identification

Lauer, Fabien; Bloch, Gérard

doi:10.1007/978-3-540-78929-1_24

Cited by 34 publications

(37 citation statements)

References 22 publications

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“…A large class of CPS are modeled as a finite collection of operational modes in a physical environment [9,14,15,17]. In this view, a CPS switches between m different modes, and the n-dimensional system state is represented as a combination of continuous and discrete components.…”

Section: Cyber-physical System Modelsmentioning

confidence: 99%

Modeling and analytics for cyber-physical systems in the age of big data

Sharma

Ivančić

Niculescu-Mizil

et al. 2014

SIGMETRICS Perform. Eval. Rev.

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In this position paper we argue that the availability of "big" monitoring data on Cyber-Physical Systems (CPS) is challenging the traditional CPS modeling approaches by violating their fundamental assumptions. However, big data also brings unique opportunities in its wake by enabling new modeling and analytics approaches as well as facilitating novel applications. We highlight a few key challenges and opportunities, and outline research directions for addressing them. To provide a proper context, we also summarize CPS modeling approaches, and discuss how modeling and analytics for CPS differs from general purpose IT systems.

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Section: Cyber-physical System Modelsmentioning

confidence: 99%

Modeling and analytics for cyber-physical systems in the age of big data

Sharma

Ivančić

Niculescu-Mizil

et al. 2014

SIGMETRICS Perform. Eval. Rev.

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“…. , n, where {f j } are affine functions ⋆ A preliminary version of this paper was presented at the 15th IFAC symposium on system identification, Saint-Malo, France, July [6][7][8]2009. ⋆⋆ This work was partially supported by ANR project ArHyCo, Programme "Systèmes Embarqués et Grandes Infrastructures" -ARPEGE, ANR-2008 SEGI 004 01-30011459, and by the grant NSF CNS 0931805.…”

Section: Introductionmentioning

confidence: 99%

“…However, the algebraic method is rather sensitive to noise compared to the other approaches. Inspired by the algebraic approach, the Support Vector Regression (SVR) approach [6,7] provides a convenient way of dealing with noisy data and small sample sizes by incorporating regularization into the optimization framework. However, it optimizes over a number of variables that grows with the number of data points, thus it is limited to small data sets.…”

Section: Introductionmentioning

confidence: 99%

A continuous optimization framework for hybrid system identification

2011

Self Cite

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We propose a new framework for hybrid system identification, which relies on continuous optimization. This framework is based on the minimization of a cost function that can be chosen as either the minimum or the product of loss functions. The former is inspired by traditional estimation methods, while the latter is inspired by recent algebraic and support vector regression approaches to hybrid system identification. In both cases, the identification problem is recast as a continuous optimization program involving only the real parameters of the model as variables, thus avoiding the use of discrete optimization. This program can be solved efficiently by using standard optimization methods even for very large data sets. In addition, the proposed framework easily incorporates robustness to different kinds of outliers through the choice of the loss function.

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“…To the best of our knowledge, the first approach to deal with nonlinear hybrid system identification without prior knowledge of the nonlinearities was proposed in [5] as an extension to the support vector regression-based method [6] which is limited to small data sets. Further extending these works in the framework of [4] resulted in the first algorithm for nonlinear hybrid system identification for large data sets as described in [7].…”

Section: Introductionmentioning

confidence: 99%

Reduced-Size Kernel Models for Nonlinear Hybrid System Identification

Bloch

Lauer

2011

IEEE Trans. Neural Netw.

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Abstract-The paper focuses on the identification of nonlinear hybrid dynamical systems, i.e., systems switching between multiple nonlinear dynamical behaviors. Thus the aim is to learn an ensemble of submodels from a single set of inputoutput data in a regression setting with no prior knowledge on the grouping of the data points into similar behaviors. To be able to approximate arbitrary nonlinearities, kernel submodels are considered. However, in order to maintain efficiency when applying the method to large data sets, a preprocessing step is required in order to fix the submodel sizes and limit the number of optimization variables. This paper proposes four approaches, respectively inspired by the fixed-size least-squares support vector machines, the feature vector selection method, the kernel principal component regression and a modification of the latter, in order to deal with this issue and build sparse kernel submodels. These are compared in numerical experiments, which show that the proposed approach achieves the simultaneous classification of data points and approximation of the nonlinear behaviors in an efficient and accurate manner.

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Switched and PieceWise Nonlinear Hybrid System Identification

Cited by 34 publications

References 22 publications

Modeling and analytics for cyber-physical systems in the age of big data

Modeling and analytics for cyber-physical systems in the age of big data

A continuous optimization framework for hybrid system identification

Reduced-Size Kernel Models for Nonlinear Hybrid System Identification

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