TY - GEN
T1 - Multi-step ahead prediction of Lorenz's chaotic system using SOM ELM-RBFNN
AU - Faqih, Akhmad
AU - Kamanditya, Bharindra
AU - Kusumoputro, Benyamin
N1 - Funding Information:
ACKNOWLEDGEMENT This research was supported by PITTA program from DPRM Universitas Indonesia with contract No. 2398/UN2. R3.1/HKP.05.00/2018.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/17
Y1 - 2018/8/17
N2 - Solving a chaotic problem is one of the application field in studying the characteristics of a nonlinear dynamical system, and actually, many real-world applications are related with a chaotic problem. In order to characterize the chaotic system, deriving the relationship between the linearity and the nonlinearity of the system is necessary, however, determining the mathematical description of a real-world chaotic system is still difficult due to insufficient basic physical phenomena. It is therefore, artificial neural networks approach is developed recently. Multi-step ahead prediction of time series problem is one of the most challenging issues for machine learning methods to solve the chaotic data prediction, especially for its higher prediction rates. In this paper, a modified Radial Basis Function Neural Network (RBFNN) with Extreme Learning Mechanism (ELM) is developed and being tested for a prediction of the future state of a chaotic problem. Experiment results show that the proposed method could provide the optimum RBF parameters with more simple but precise prediction method for up to 60 steps ahead.
AB - Solving a chaotic problem is one of the application field in studying the characteristics of a nonlinear dynamical system, and actually, many real-world applications are related with a chaotic problem. In order to characterize the chaotic system, deriving the relationship between the linearity and the nonlinearity of the system is necessary, however, determining the mathematical description of a real-world chaotic system is still difficult due to insufficient basic physical phenomena. It is therefore, artificial neural networks approach is developed recently. Multi-step ahead prediction of time series problem is one of the most challenging issues for machine learning methods to solve the chaotic data prediction, especially for its higher prediction rates. In this paper, a modified Radial Basis Function Neural Network (RBFNN) with Extreme Learning Mechanism (ELM) is developed and being tested for a prediction of the future state of a chaotic problem. Experiment results show that the proposed method could provide the optimum RBF parameters with more simple but precise prediction method for up to 60 steps ahead.
KW - Chaos
KW - Chaotic System
KW - Extreme Learning Machine
KW - Multi-Step Ahead Prediction
KW - Radial Basis Function
KW - Self-Organizing Maps
UR - http://www.scopus.com/inward/record.url?scp=85053117661&partnerID=8YFLogxK
U2 - 10.1109/CITS.2018.8440187
DO - 10.1109/CITS.2018.8440187
M3 - Conference contribution
AN - SCOPUS:85053117661
SN - 9781538640753
T3 - CITS 2018 - 2018 International Conference on Computer, Information and Telecommunication Systems
BT - CITS 2018 - 2018 International Conference on Computer, Information and Telecommunication Systems
A2 - Obaidat, Mohammad S.
A2 - Nicopolitidis, Petros
A2 - Obaidat, Mohammad S.
A2 - Lorenz, Pascal
A2 - Hsiao, Kuei-Fang
A2 - Obaidat, Mohammad S.
A2 - Cascado-Caballero, Daniel
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 International Conference on Computer, Information and Telecommunication Systems, CITS 2018
Y2 - 11 July 2018 through 13 July 2018
ER -