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

Wen Wang, Youlun Xiong, Hong Wang, Hong Yue

Research output: Chapter in Book/Report/Conference proceedingChapter

3 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.
LanguageEnglish
Title of host publicationIntelligent Robotics and Applications
EditorsCaihua Xiong, Honghai Liu, Yongan Huang, Youlun Xiong
Place of PublicationBerlin
PublisherSpringer
Pages820-828
Number of pages9
ISBN (Print)9783540885160
DOIs
Publication statusPublished - 2008

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume5315
ISSN (Print)0302-9743

Fingerprint

Multilayer neural networks
MIMO systems
Probability density function
Neural networks
Principal component analysis
Controllers

Keywords

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

Cite this

Wang, W., Xiong, Y., Wang, H., & Yue, H. (2008). MIMO probability density function control using simple LOG-MLP neural networks. In C. Xiong, H. Liu, Y. Huang, & Y. Xiong (Eds.), Intelligent Robotics and Applications (pp. 820-828). (Lecture Notes in Computer Science; Vol. 5315). Berlin: Springer. https://doi.org/10.1007/978-3-540-88518-4_88
Wang, Wen ; Xiong, Youlun ; Wang, Hong ; Yue, Hong. / MIMO probability density function control using simple LOG-MLP neural networks. Intelligent Robotics and Applications. editor / Caihua Xiong ; Honghai Liu ; Yongan Huang ; Youlun Xiong. Berlin : Springer, 2008. pp. 820-828 (Lecture Notes in Computer Science).
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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.",
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Wang, W, Xiong, Y, Wang, H & Yue, H 2008, MIMO probability density function control using simple LOG-MLP neural networks. in C Xiong, H Liu, Y Huang & Y Xiong (eds), Intelligent Robotics and Applications. Lecture Notes in Computer Science, vol. 5315, Springer, Berlin, pp. 820-828. https://doi.org/10.1007/978-3-540-88518-4_88

MIMO probability density function control using simple LOG-MLP neural networks. / Wang, Wen; Xiong, Youlun; Wang, Hong; Yue, Hong.

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 proceedingChapter

TY - CHAP

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

AU - Wang, Wen

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AU - Yue, Hong

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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

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T3 - Lecture Notes in Computer Science

SP - 820

EP - 828

BT - Intelligent Robotics and Applications

A2 - Xiong, Caihua

A2 - Liu, Honghai

A2 - Huang, Yongan

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PB - Springer

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Wang W, Xiong Y, Wang H, Yue H. MIMO probability density function control using simple LOG-MLP neural networks. In Xiong C, Liu H, Huang Y, Xiong Y, editors, Intelligent Robotics and Applications. Berlin: Springer. 2008. p. 820-828. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-540-88518-4_88