平稳和非平稳环境下的在线自适应RBF网络

2000 10th Mediterranean Electrotechnical Conference. Information Technology and Electrotechnology for the Mediterranean Countries. Proceedings. MeleCon 2000 (Cat. No.00CH37099) Pub Date : 2000-05-29 DOI:10.1109/MELCON.2000.879968

B. Todorovic, M. Stankovic, S. Todorovic-Zarkula

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摘要

利用扩展卡尔曼滤波(EKF)实现了RBF网络参数和结构的顺序自适应。通过卡尔曼滤波的一致性检验，得到了网络生长的判据，导出了由EKF估计参数的网络的最优脑外科医生和最优脑损伤修剪方法。神经元/连接修剪的标准基于统计参数显著性检验。

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On-line adaptive RBF network in stationary and nonstationary environment

Sequential adaptation of RBF network parameters and structure is achieved using extended Kalman filter (EKF). Criterion for network growing is obtained from Kalman filter's consistency test The optimal brain surgeon and optimal brain damage pruning methods are derived for networks which parameters are estimated by EKF. Criteria for neurons/connections pruning are based on the statistical parameter significance test.

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2000 10th Mediterranean Electrotechnical Conference. Information Technology and Electrotechnology for the Mediterranean Countries. Proceedings. MeleCon 2000 (Cat. No.00CH37099)

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