47,117 research outputs found
Identification of Structured LTI MIMO State-Space Models
The identification of structured state-space model has been intensively
studied for a long time but still has not been adequately addressed. The main
challenge is that the involved estimation problem is a non-convex (or bilinear)
optimization problem. This paper is devoted to developing an identification
method which aims to find the global optimal solution under mild computational
burden. Key to the developed identification algorithm is to transform a
bilinear estimation to a rank constrained optimization problem and further a
difference of convex programming (DCP) problem. The initial condition for the
DCP problem is obtained by solving its convex part of the optimization problem
which happens to be a nuclear norm regularized optimization problem. Since the
nuclear norm regularized optimization is the closest convex form of the
low-rank constrained estimation problem, the obtained initial condition is
always of high quality which provides the DCP problem a good starting point.
The DCP problem is then solved by the sequential convex programming method.
Finally, numerical examples are included to show the effectiveness of the
developed identification algorithm.Comment: Accepted to IEEE Conference on Decision and Control (CDC) 201
Penguasaan kemahiran generik di kalangan graduan hospitaliti di politeknik : satu kajian berkenaan keperluan industri perhotelan, persepsi pensyarah dan pelajar
Kajian yang dijalankan ini bertujuan untuk mengenal pasti kepentingan
kemahiran generik mengikut keperluan industri perhotelan di Malaysia dengan persepsi pensyarah dan persepsi pelajar Jabatan Hospitaliti. Oleh kerana matlamat kurikulum pendidikan kini adalah untuk melahirkan graduan yang dapat memenuhi keperluan pihak industri, maka kajian ini dijalankan untuk menilai hubungan di antara keperluan industri perhotelan di Malaysia dengan persepsi pensyarah dan pelajar Jabatan Hospitaliti di Politeknik. Terdapat 13 kemahiran generik yang diperolehi daripada Kementerian Pelajaran dan Latihan Ontario (1997) dijadikan
sebagai skop kepada instrumen kajian. Responden kajian terdiri daripada tiga pihak utama iaitu industri perhotelan di Malaysia yang melibatkan 40 buah hotel yang diwakili oleh MAH Chapter dan jawatankuasa dalam Malaysian Associated of Hotel (MAH), pensyarah Unit Hotel dan Katering dan pelajar semester akhir Diploma Hotel dan Katering di Politeknik Johor Bahru, Johor dan Politeknik Merlimau, Melaka. Kajian rintis yang dijalankan menunjukkan nilai Alpha Cronbach pada 0.8781. Data yang diperolehi dianalisis secara deskriptif dan inferensi dengan menggunakan perisian Statistical Package for Social Science (SPSS) versi 11.5. Melalui dapatan kajian, satu senarai berkenaan kemahiran generik yang diperlukan
oleh industri perhotelan telah dapat dihasilkan. Selain itu, senarai kemahiran generik menurut persepsi pensyarah dan juga persepsi pelajar turut dihasilkan. Hasil statistik dan graf garis yang diperolehi menunjukkan terdapat perbezaan di antara kemahiran generik yang diperlukan oleh industri perhotelan di Malaysia dengan kemahiran generik menurut persepsi pensyarah dan persepsi pelajar Politeknik. Dapatan analisis menggunakan korelasi Pearson mendapati bahawa tidak terdapat
perhubungan yang signifikan di antara kemahiran generik yang diperlukan oleh industri perhotelan dengan persepsi pensyarah dan persepsi pelajar. Namun begitu, terdapat hubungan yang signifikan di antara persepsi pensyarah dengan persepsi pelajar berkenaan dengan amalan kemahiran generik di Politeknik
Parameter reduction in nonlinear state-space identification of hysteresis
Hysteresis is a highly nonlinear phenomenon, showing up in a wide variety of
science and engineering problems. The identification of hysteretic systems from
input-output data is a challenging task. Recent work on black-box polynomial
nonlinear state-space modeling for hysteresis identification has provided
promising results, but struggles with a large number of parameters due to the
use of multivariate polynomials. This drawback is tackled in the current paper
by applying a decoupling approach that results in a more parsimonious
representation involving univariate polynomials. This work is carried out
numerically on input-output data generated by a Bouc-Wen hysteretic model and
follows up on earlier work of the authors. The current article discusses the
polynomial decoupling approach and explores the selection of the number of
univariate polynomials with the polynomial degree, as well as the connections
with neural network modeling. We have found that the presented decoupling
approach is able to reduce the number of parameters of the full nonlinear model
up to about 50\%, while maintaining a comparable output error level.Comment: 24 pages, 8 figure
Data-driven computation of invariant sets of discrete time-invariant black-box systems
We consider the problem of computing the maximal invariant set of
discrete-time black-box nonlinear systems without analytic dynamical models.
Under the assumption that the system is asymptotically stable, the maximal
invariant set coincides with the domain of attraction. A data-driven framework
relying on the observation of trajectories is proposed to compute
almost-invariant sets, which are invariant almost everywhere except a small
subset. Based on these observations, scenario optimization problems are
formulated and solved. We show that probabilistic invariance guarantees on the
almost-invariant sets can be established. To get explicit expressions of such
sets, a set identification procedure is designed with a verification step that
provides inner and outer approximations in a probabilistic sense. The proposed
data-driven framework is illustrated by several numerical examples.Comment: A shorter version with the title "Scenario-based set invariance
verification for black-box nonlinear systems" is published in the IEEE
Control Systems Letters (L-CSS
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