### Abstract

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 | |

Publication status | Published - 2008 |

### Publication series

Name | Lecture Notes in Computer Science |
---|---|

Publisher | Springer |

Volume | 5315 |

ISSN (Print) | 0302-9743 |

### Fingerprint

### 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

*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

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*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.

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 -