TY - GEN
T1 - Mackey-glass chaotic time series prediction using modified rbf neural networks
AU - Faqih, Akhmad
AU - Lianto, Aldo Pratama
AU - Kusumoputro, Benyamin
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/1/10
Y1 - 2019/1/10
N2 - The characteristics of a nonlinear dynamical system within chaotic system is more intensely studied recently, due to many real-world applications of the nonlinear chaotic system are increasing. For characterizing the ordinary system, usually the relationship between the linearity and the nonlinearity of parameters in the system is needed to be firstly derived, however, creating the mathematical model of the real chaotic system is still a problematic since insufficient basic physical phenomena should be analyzed. Hence, artificial neural networks approach that performed based on nonlinear mathematical model is quite adequate to be used to analyze the chaotic phenomena within the system. Solving the multi-step ahead prediction problem of time series chaotic system is one of the top challenging issues, especially on how to obtain a higher prediction rate. In this paper, a modified Radial Basis Function Neural Network (RBF-NN) is developed and be tested for predicting the future state of a Mackey-Glass equation as the chaotic system. Results experiments show that using training testing paradigm of 50%:50%, the calculated of confidence level accuracy of the neural-predictor system is satisfied for up to 30-steps ahead prediction.
AB - The characteristics of a nonlinear dynamical system within chaotic system is more intensely studied recently, due to many real-world applications of the nonlinear chaotic system are increasing. For characterizing the ordinary system, usually the relationship between the linearity and the nonlinearity of parameters in the system is needed to be firstly derived, however, creating the mathematical model of the real chaotic system is still a problematic since insufficient basic physical phenomena should be analyzed. Hence, artificial neural networks approach that performed based on nonlinear mathematical model is quite adequate to be used to analyze the chaotic phenomena within the system. Solving the multi-step ahead prediction problem of time series chaotic system is one of the top challenging issues, especially on how to obtain a higher prediction rate. In this paper, a modified Radial Basis Function Neural Network (RBF-NN) is developed and be tested for predicting the future state of a Mackey-Glass equation as the chaotic system. Results experiments show that using training testing paradigm of 50%:50%, the calculated of confidence level accuracy of the neural-predictor system is satisfied for up to 30-steps ahead prediction.
KW - Chaotic system
KW - Mackey-glass equation
KW - Multi-step ahead prediction
KW - Radial basis function
KW - Self-organized maps
UR - http://www.scopus.com/inward/record.url?scp=85063574067&partnerID=8YFLogxK
U2 - 10.1145/3305160.3305187
DO - 10.1145/3305160.3305187
M3 - Conference contribution
AN - SCOPUS:85063574067
T3 - ACM International Conference Proceeding Series
SP - 7
EP - 11
BT - Proceedings of the 2019 2nd International Conference on Software Engineering and Information Management, ICSIM 2019 - Workshop 2019 2nd International Conference on Big Data and Smart Computing, ICBDSC 2019
PB - Association for Computing Machinery
T2 - 2nd International Conference on Software Engineering and Information Management, ICSIM 2019 - and its Workshop 2019 2nd International Conference on Big Data and Smart Computing, ICBDSC 2019
Y2 - 10 January 2019 through 13 January 2019
ER -