MIMO probability density function control using simple LOG-MLP neural networks

Wen Wang; Youlun Xiong; Hong Wang; Hong Yue

doi:10.1007/978-3-540-88518-4_88

MIMO probability density function control using simple LOG-MLP neural networks

Wen Wang, Youlun Xiong, Hong Wang, Hong Yue

Electronic And Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Chapter

4 Citations (Scopus)

Abstract

This paper presents a new model for the control of multivariable output probability density function (PDF). Firstly, a Multi-Layer Perceptron (MLP) neural network is adopted to approximate the static output PDF of the MIMO systems. Nonlinear principal component analysis (NLPCA) is then used to reduce the order of the obtained static neural network model and the dynamics of the system is considered based on the lower-order model. After this, an integrated solution is provided to set up the system with lower-order dynamics for the purpose of stochastic distribution control. The controller design is then presented in detail. Finally, a simulation example is given to demonstrate the effectiveness of the method and encouraging results have been obtained.

Original language	English
Title of host publication	Intelligent Robotics and Applications
Editors	Caihua Xiong, Honghai Liu, Yongan Huang, Youlun Xiong
Place of Publication	Berlin
Publisher	Springer
Pages	820-828
Number of pages	9
ISBN (Print)	9783540885160
DOIs	https://doi.org/10.1007/978-3-540-88518-4_88
Publication status	Published - 2008

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	5315
ISSN (Print)	0302-9743

Keywords

MIMO
probability density
function control
neural networks
LOG-MLP
nonlinear principal component analysis
stochastic systems
combined probability density function
MLP neural networks

Access to Document

10.1007/978-3-540-88518-4_88

Cite this

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title = "MIMO probability density function control using simple LOG-MLP neural networks",

abstract = "This paper presents a new model for the control of multivariable output probability density function (PDF). Firstly, a Multi-Layer Perceptron (MLP) neural network is adopted to approximate the static output PDF of the MIMO systems. Nonlinear principal component analysis (NLPCA) is then used to reduce the order of the obtained static neural network model and the dynamics of the system is considered based on the lower-order model. After this, an integrated solution is provided to set up the system with lower-order dynamics for the purpose of stochastic distribution control. The controller design is then presented in detail. Finally, a simulation example is given to demonstrate the effectiveness of the method and encouraging results have been obtained.",

keywords = "MIMO, probability density, function control, neural networks, LOG-MLP, nonlinear principal component analysis, stochastic systems, combined probability density function, MLP neural networks",

author = "Wen Wang and Youlun Xiong and Hong Wang and Hong Yue",

year = "2008",

doi = "10.1007/978-3-540-88518-4_88",

language = "English",

isbn = "9783540885160",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "820--828",

editor = "Xiong, {Caihua } and Liu, {Honghai } and Huang, {Yongan } and Xiong, {Youlun }",

booktitle = "Intelligent Robotics and Applications",

}

MIMO probability density function control using simple LOG-MLP neural networks. / Wang, Wen; Xiong, Youlun; Wang, Hong et al.
Intelligent Robotics and Applications. ed. / Caihua Xiong; Honghai Liu; Yongan Huang; Youlun Xiong. Berlin: Springer, 2008. p. 820-828 (Lecture Notes in Computer Science; Vol. 5315).

Research output: Chapter in Book/Report/Conference proceeding › Chapter

TY - CHAP

T1 - MIMO probability density function control using simple LOG-MLP neural networks

AU - Wang, Wen

AU - Xiong, Youlun

AU - Wang, Hong

AU - Yue, Hong

PY - 2008

Y1 - 2008

N2 - This paper presents a new model for the control of multivariable output probability density function (PDF). Firstly, a Multi-Layer Perceptron (MLP) neural network is adopted to approximate the static output PDF of the MIMO systems. Nonlinear principal component analysis (NLPCA) is then used to reduce the order of the obtained static neural network model and the dynamics of the system is considered based on the lower-order model. After this, an integrated solution is provided to set up the system with lower-order dynamics for the purpose of stochastic distribution control. The controller design is then presented in detail. Finally, a simulation example is given to demonstrate the effectiveness of the method and encouraging results have been obtained.

AB - This paper presents a new model for the control of multivariable output probability density function (PDF). Firstly, a Multi-Layer Perceptron (MLP) neural network is adopted to approximate the static output PDF of the MIMO systems. Nonlinear principal component analysis (NLPCA) is then used to reduce the order of the obtained static neural network model and the dynamics of the system is considered based on the lower-order model. After this, an integrated solution is provided to set up the system with lower-order dynamics for the purpose of stochastic distribution control. The controller design is then presented in detail. Finally, a simulation example is given to demonstrate the effectiveness of the method and encouraging results have been obtained.

KW - MIMO

KW - probability density

KW - function control

KW - neural networks

KW - LOG-MLP

KW - nonlinear principal component analysis

KW - stochastic systems

KW - combined probability density function

KW - MLP neural networks

U2 - 10.1007/978-3-540-88518-4_88

DO - 10.1007/978-3-540-88518-4_88

M3 - Chapter

SN - 9783540885160

T3 - Lecture Notes in Computer Science

SP - 820

EP - 828

BT - Intelligent Robotics and Applications

A2 - Xiong, Caihua

A2 - Liu, Honghai

A2 - Huang, Yongan

A2 - Xiong, Youlun

PB - Springer

CY - Berlin

ER -

MIMO probability density function control using simple LOG-MLP neural networks

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Keywords

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