Training the minimal resource allocation network with the unscented Kalman filter

被引：0

作者：

Zhang, Y ^{[1
]}

Wu, YQ ^{[1
]}

Wan, GJ ^{[1
]}

Zhang, WQ ^{[1
]}

机构：

[1] Nanchang Univ, Elect Informat & Engn Fac, Nanchang 330029, Peoples R China

来源：

ICEMI 2005: CONFERENCE PROCEEDINGS OF THE SEVENTH INTERNATIONAL CONFERENCE ON ELECTRONIC MEASUREMENT & INSTRUMENTS, VOL 4 | 2005年

关键词：

UKF; MRAN; EKF; MUKF;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The MARN is a sequential learning RBF network and has the ability to grow and prune the hidden neurons to realize a minimal network structure. This paper proposes the use of Unscented Kalman Filter (UKF) for training the MRAN parameters i.e. centers, radii and weights of all the hidden neurons. In order to reduce UKF computational load, a modification algorithm is then presented. In our simulation, we implemented the MRAN trained with UKF and the MRAN trained with EKF for states estimation. The performance of the MRAN trained with UKF is superior than the MRAN trained with EKF.

引用

页码：483 / 487

页数：5