Structurally adaptive RBF network in nonstationary time series prediction

被引：6

作者：

Todorovic, B ^{[1
]}

Stankovic, M ^{[1
]}

Todorovic-Zarkula, S ^{[1
]}

机构：

[1] Univ Nish, Fac Occupat Safety, Nish, Yugoslavia

来源：

IEEE 2000 ADAPTIVE SYSTEMS FOR SIGNAL PROCESSING, COMMUNICATIONS, AND CONTROL SYMPOSIUM - PROCEEDINGS | 2000年

关键词：

D O I：

10.1109/ASSPCC.2000.882475

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：

A sequentially adaptive Radial Basis Function (RBF) network is applied to the nonstationary time series prediction. Sequential adaptation of parameters and structure is achieved using extended Kalman filter. Criterion for network growing is obtained from Kalman filter's consistency rest. 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. Prediction of nonstationary logistic map and Lorenz rime series is considered.

引用

页码：224 / 229

页数：6

共 50 条

[1] A Time Series Prediction Method Based on Self-Adaptive RBF Neural Network
Xiao, Ding
Li, Xu
Lin, Xiuqin
Shi, Chuan
[J]. PROCEEDINGS OF 2015 4TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND NETWORK TECHNOLOGY (ICCSNT 2015), 2015, : 685 - 688
[2] Fast Adaptive Gradient RBF Networks For Online Learning of Nonstationary Time Series
Liu, Tong
Chen, Sheng
Liang, Shan
Gan, Shaojun
Harris, Chirs J.
[J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2020, 68 : 2015 - 2030
[3] A seasonal GRBF network for nonstationary time series prediction
Wang, Hao
Wang, Junpu
Tian, Weifeng
[J]. MEASUREMENT SCIENCE AND TECHNOLOGY, 2006, 17 (10) : 2806 - 2810
[4] Nonlinear Time Series Prediction by Using RBF Network
Zhu, Liqiang
[J]. ADVANCES IN NEURAL NETWORKS - ISNN 2009, PT 1, PROCEEDINGS, 2009, 5551 : 901 - 908
[5] On Adaptive Prediction of Nonstationary and Inconsistent Large Time Series Data
Pelech-Pilichowski, T.
[J]. 2018 41ST INTERNATIONAL CONVENTION ON INFORMATION AND COMMUNICATION TECHNOLOGY, ELECTRONICS AND MICROELECTRONICS (MIPRO), 2018, : 1260 - 1265
[6] Multivariate Time Series Prediction Based on a Simple RBF Network
Xi Jianhui
Wang Haidan
Jiang Liying
[J]. MACHINE DESIGN AND MANUFACTURING ENGINEERING, 2012, 566 : 97 - 102
[7] A Fast Adaptive Tunable RBF Network For Nonstationary Systems
Chen, Hao
Gong, Yu
Hong, Xia
Chen, Sheng
[J]. IEEE TRANSACTIONS ON CYBERNETICS, 2016, 46 (12) : 2683 - 2692
[8] Markovian RNN: An Adaptive Time Series Prediction Network With HMM-Based Switching for Nonstationary Environments
Ilhan, Fatih
Karaahmetoglu, Oguzhan
Balaban, Ismail
Kozat, Suleyman Serdar
[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 34 (02) : 715 - 728
[9] Adaptive Broad Echo State Network for Nonstationary Time Series Forecasting
Liu, Wen-Jie
Bai, Yu-Ting
Jin, Xue-Bo
Su, Ting-Li
Kong, Jian-Lei
[J]. MATHEMATICS, 2022, 10 (17)
[10] Center selection for RBF neural network in prediction of nonlinear time series
Lu, YH
Wu, CG
Liang, YC
[J]. 2003 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-5, PROCEEDINGS, 2003, : 1355 - 1359

← 1 2 3 4 5 →