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المؤلفRizvi, S.Z.
المؤلفMohammadpour, J.
المؤلفToth, R.
المؤلفMeskin, Nader
تاريخ الإتاحة2022-04-14T08:45:43Z
تاريخ النشر2014
اسم المنشور2014 European Control Conference, ECC 2014
المصدرScopus
المعرّفhttp://dx.doi.org/10.1109/ECC.2014.6862571
معرّف المصادر الموحدhttp://hdl.handle.net/10576/29813
الملخصThis paper proposes a method for reduction of scheduling dependency in linear parameter-varying (LPV) systems. In particular, both the dimension of the scheduling variable and the corresponding scheduling region are shrunk using kernel-based principal component analysis (PCA). Kernel PCA corresponds to linear PCA that is performed in a high-dimensional feature space, allowing the extension of linear PCA to nonlinear dimensionality reduction. Hence, it enables the reduction of complicated coefficient dependencies which cannot be simplified in a linear subspace, giving kernel PCA an advantage over other linear techniques. This corresponds to mapping the original scheduling variables to a set of lower dimensional variables via a nonlinear mapping. However, to recover the original coefficient functions of the model, this nonlinear mapping is needed to be inverted. Such an inversion is not straightforward. The reduced scheduling variables are a nonlinear expansion of the original scheduling variables into a high-dimensional feature space, an inverse mapping for which is not available. Therefore, we cannot generally assert that such an expansion has a 'pre-image' in the original scheduling region. While certain pre-image approximation algorithms are found in the literature for Gaussian kernel-based PCA, we aim to generalize the pre-image estimation algorithm to other commonly used kernels, and formulate an iterative pre-image estimation rule. Finally, we consider the case study of a physical system described by an LPV model and compare the performance of linear and kernel PCA-based LPV model reduction. 2014 EUCA.
راعي المشروعQatar National Research Fund
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعApproximation algorithms
Dimensionality reduction
Iterative methods
Linear systems
Mapping
Scheduling
Coefficient functions
High-dimensional feature space
Kernel based principal component analysis
Linear parameter varying systems
Non-linear expansion
Nonlinear dimensionality reduction
Nonlinear mappings
Scheduling variable
Nonlinear analysis
العنوانParameter Set-mapping using kernel-based PCA for linear parameter-varying systems
النوعConference Paper
الصفحات2744-2749
dc.accessType Abstract Only


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