### 抄録

This paper presents theoretical and experimental investigation of active noise control (ANC) in free space using recurrent neural networks. A learning algorithm for diagonal recurrent neural networks based on extended Kalman filter is developed and is referred to as diagonal recurrent extended Kalman filter (DREKF) algorithm. Based on DREKF, new control algorithm suited for ANC is developed to handle nonlinearity inherently arising in this application. Real-time experiment using floating point digital signal processor is carried out for both identification and control tasks required in ANC. The results show that the number of neurons in neural network can be reduced by introducing the diagonal recurrent elements, without deteriorating the system performance, and that DREKF produces better performance than linear adaptive controller in compensating the secondary path nonlinearity.

元の言語 | English |
---|---|

ページ（範囲） | 267-276 |

ページ数 | 10 |

ジャーナル | Control and Intelligent Systems |

巻 | 36 |

発行部数 | 3 |

出版物ステータス | Published - 2008 |

### Fingerprint

### ASJC Scopus subject areas

- Hardware and Architecture
- Control and Systems Engineering

### これを引用

*Control and Intelligent Systems*,

*36*(3), 267-276.

**Active noise control in free space using recurrent neural networks with EKF algorithm.** / Bambang, R. T.; Uchida, Kenko; Yacoub, R. R.

研究成果: Article

*Control and Intelligent Systems*, 巻. 36, 番号 3, pp. 267-276.

}

TY - JOUR

T1 - Active noise control in free space using recurrent neural networks with EKF algorithm

AU - Bambang, R. T.

AU - Uchida, Kenko

AU - Yacoub, R. R.

PY - 2008

Y1 - 2008

N2 - This paper presents theoretical and experimental investigation of active noise control (ANC) in free space using recurrent neural networks. A learning algorithm for diagonal recurrent neural networks based on extended Kalman filter is developed and is referred to as diagonal recurrent extended Kalman filter (DREKF) algorithm. Based on DREKF, new control algorithm suited for ANC is developed to handle nonlinearity inherently arising in this application. Real-time experiment using floating point digital signal processor is carried out for both identification and control tasks required in ANC. The results show that the number of neurons in neural network can be reduced by introducing the diagonal recurrent elements, without deteriorating the system performance, and that DREKF produces better performance than linear adaptive controller in compensating the secondary path nonlinearity.

AB - This paper presents theoretical and experimental investigation of active noise control (ANC) in free space using recurrent neural networks. A learning algorithm for diagonal recurrent neural networks based on extended Kalman filter is developed and is referred to as diagonal recurrent extended Kalman filter (DREKF) algorithm. Based on DREKF, new control algorithm suited for ANC is developed to handle nonlinearity inherently arising in this application. Real-time experiment using floating point digital signal processor is carried out for both identification and control tasks required in ANC. The results show that the number of neurons in neural network can be reduced by introducing the diagonal recurrent elements, without deteriorating the system performance, and that DREKF produces better performance than linear adaptive controller in compensating the secondary path nonlinearity.

KW - Active noise control

KW - Diagonal recurrent neural networks

KW - Digital signal processor

KW - Extended Kalman filter

KW - Free space

KW - Nonlinearity

UR - http://www.scopus.com/inward/record.url?scp=50249140163&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=50249140163&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:50249140163

VL - 36

SP - 267

EP - 276

JO - Mechatronic Systems and Control

JF - Mechatronic Systems and Control

SN - 2561-1771

IS - 3

ER -