Mean and entropy of b-spline PDF models: analysis and design

J. Zhou; H. Yue; H. Wang

doi:10.3182/20050703-6-CZ-1902.00368

Mean and entropy of b-spline PDF models: analysis and design

J. Zhou, H. Yue, H. Wang

Electronic And Electrical Engineering

Research output: Contribution to conference › Paper

Abstract

For continuous probability density functions (PDFs) approximated by the B-spline basis functions, the relationships between the B-spline weights, the entropy and the mean have been analyzed in detail. It shows the different characteristics of the entropy with and without mean constraint. A minimum entropy controller subjected to mean constraint is developed by taking the performance function as a Lyapunov function and ensuring the negativeness of its first-order derivative. Simulation examples are included to validate the analysis results and evaluate the closed-loop control performance.

Original language	English
Pages	1 - 6
Number of pages	6
DOIs	https://doi.org/10.3182/20050703-6-CZ-1902.00368
Publication status	Published - 2005
Event	16th IFAC World Congress, 2005 - Prague, Czech Republic Duration: 4 Jul 2005 → 8 Jul 2005

Conference

Conference	16th IFAC World Congress, 2005
Country	Czech Republic
City	Prague
Period	4/07/05 → 8/07/05

Keywords

dynamic stochastic systems
probability density function (PDF)
B-spline neural network
minimum entropy
mean constraint

Access to Document

10.3182/20050703-6-CZ-1902.00368

Fingerprint Dive into the research topics of 'Mean and entropy of b-spline PDF models: analysis and design'. Together they form a unique fingerprint.

Cite this

@conference{d8c951c890c54d3bb3087ec07cca8fe2,

title = "Mean and entropy of b-spline PDF models: analysis and design",

abstract = "For continuous probability density functions (PDFs) approximated by the B-spline basis functions, the relationships between the B-spline weights, the entropy and the mean have been analyzed in detail. It shows the different characteristics of the entropy with and without mean constraint. A minimum entropy controller subjected to mean constraint is developed by taking the performance function as a Lyapunov function and ensuring the negativeness of its first-order derivative. Simulation examples are included to validate the analysis results and evaluate the closed-loop control performance.",

keywords = "dynamic stochastic systems, probability density function (PDF), B-spline neural network, minimum entropy, mean constraint",

author = "J. Zhou and H. Yue and H. Wang",

year = "2005",

doi = "10.3182/20050703-6-CZ-1902.00368",

language = "English",

pages = "1 -- 6",

note = "16th IFAC World Congress, 2005 ; Conference date: 04-07-2005 Through 08-07-2005",

}

TY - CONF

T1 - Mean and entropy of b-spline PDF models: analysis and design

AU - Zhou, J.

AU - Yue, H.

AU - Wang, H.

PY - 2005

Y1 - 2005

N2 - For continuous probability density functions (PDFs) approximated by the B-spline basis functions, the relationships between the B-spline weights, the entropy and the mean have been analyzed in detail. It shows the different characteristics of the entropy with and without mean constraint. A minimum entropy controller subjected to mean constraint is developed by taking the performance function as a Lyapunov function and ensuring the negativeness of its first-order derivative. Simulation examples are included to validate the analysis results and evaluate the closed-loop control performance.

AB - For continuous probability density functions (PDFs) approximated by the B-spline basis functions, the relationships between the B-spline weights, the entropy and the mean have been analyzed in detail. It shows the different characteristics of the entropy with and without mean constraint. A minimum entropy controller subjected to mean constraint is developed by taking the performance function as a Lyapunov function and ensuring the negativeness of its first-order derivative. Simulation examples are included to validate the analysis results and evaluate the closed-loop control performance.

KW - dynamic stochastic systems

KW - probability density function (PDF)

KW - B-spline neural network

KW - minimum entropy

KW - mean constraint

U2 - 10.3182/20050703-6-CZ-1902.00368

DO - 10.3182/20050703-6-CZ-1902.00368

M3 - Paper

SP - 1

EP - 6

T2 - 16th IFAC World Congress, 2005

Y2 - 4 July 2005 through 8 July 2005

ER -