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

Jinglin Zhou; Hong Yue; Hong Wang

doi:10.3182/20050703-6-CZ-1902.00368

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

Jinglin Zhou, Hong Yue, Hong Wang

Electronic And Electrical Engineering

Research output: Contribution to journal › Article › peer-review

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 (from-to)	93-98
Number of pages	6
Journal	IFAC Proceedings Volumes
Volume	38
Issue number	1
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

Keywords

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

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10.3182/20050703-6-CZ-1902.00368

Cite this

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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 = "Jinglin Zhou and Hong Yue and Hong Wang",

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AU - Yue, Hong

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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

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VL - 38

SP - 93

EP - 98

IS - 1

Y2 - 4 July 2005 through 8 July 2005

ER -

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

Abstract

Keywords

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