Tuning of Type-2 Fuzzy Systems by Simulated A nnealing to Predict Time Series

In this paper, a combination of interval type-2 fuzzy system (IT2FS) models and simulated annealing are used to predict the Mackey-Glass time series by searching for the best configuration of the IT2FS. Simulated annealing is used to optimise the parameters of the antecedent and the consequent parts of the rules for a Mamdani model. Simulated annealing is combined with a method to reduce the computations associated with it using an adaptive step size. The results of the proposed methods are compared to results of a type-1 fuzzy system. Index Terms—Type-2-Fuzzy-Systems, Simulated-Annealing, Time-Series-Forecasting.

[1]  Lotfi A. Zadeh,et al.  Soft computing and fuzzy logic , 1994, IEEE Software.

[2]  Francisco Herrera,et al.  Ten years of genetic fuzzy systems: current framework and new trends , 2004, Fuzzy Sets Syst..

[3]  Hossein S. Zadeh,et al.  Soft computing in engineering design optimisation , 2006, J. Intell. Fuzzy Syst..

[4]  Kenneth H. Stokoe,et al.  Proceedings of the World Congress on Engineering 2013, WCE 2013 , 2013 .

[5]  Chulhyun Kim,et al.  Forecasting time series with genetic fuzzy predictor ensemble , 1997, IEEE Trans. Fuzzy Syst..

[6]  Dragan Kukolj,et al.  Design of adaptive Takagi-Sugeno-Kang fuzzy models , 2002, Appl. Soft Comput..

[7]  R. John,et al.  Type-2 Fuzzy Logic: A Historical View , 2007, IEEE Computational Intelligence Magazine.

[8]  R. John,et al.  Tuning fuzzy systems by simulated annealing to predict time series with added noise , 2010, 2010 UK Workshop on Computational Intelligence (UKCI).

[9]  Jerry M. Mendel,et al.  Type-2 fuzzy sets made simple , 2002, IEEE Trans. Fuzzy Syst..

[10]  Chin-Teng Lin,et al.  An online self-constructing neural fuzzy inference network and its applications , 1998, IEEE Trans. Fuzzy Syst..

[11]  Pablo Moscato,et al.  Handbook of Applied Optimization , 2000 .

[12]  L. Glass,et al.  Oscillation and chaos in physiological control systems. , 1977, Science.

[13]  Lars Nolle,et al.  On Step Width Adaptation in Simulated Annealing for Continuous Parameter Optimisation , 2001, Fuzzy Days.

[14]  J. Mendel.  Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions , 2001 .

[15]  Jonathan M. Garibaldi,et al.  Application of simulated annealing fuzzy model tuning to umbilical cord acid-base interpretation , 1999, IEEE Trans. Fuzzy Syst..

[16]  Panos M. Pardalos,et al.  Handbook of applied optimization , 2002 .

[17]  Robert Ivor John,et al.  Time series forecasting using a TSK fuzzy system tuned with simulated annealing , 2010, International Conference on Fuzzy Systems.

[18]  Yves Crama,et al.  Local Search in Combinatorial Optimization , 2018, Artificial Neural Networks.

[19]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[20]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[21]  H. Hagras,et al.  Type-2 FLCs: A New Generation of Fuzzy Controllers , 2007, IEEE Computational Intelligence Magazine.

[22]  Marco Russo,et al.  Genetic fuzzy learning , 2000, IEEE Trans. Evol. Comput..

[23]  Yskandar Hamam,et al.  Simulated annealing for fuzzy controller optimization: principles and applications , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

[24]  Sukhdev Khebbal,et al.  Intelligent Hybrid Systems , 1994 .

[25]  Anju Vyas.  Print , 2003 .

[26]  Guixi Liu,et al.  Learning and tuning of fuzzy membership functions by simulated annealing algorithm , 2000, IEEE APCCAS 2000. 2000 IEEE Asia-Pacific Conference on Circuits and Systems. Electronic Communication Systems. (Cat. No.00EX394).

[27]  Paolo Dadone,et al.  Design Optimization of Fuzzy Logic Systems , 2001 .

[28]  Steve R. White,et al.  Concepts of scale in simulated annealing , 2008 .

[29]  Ji-Chang Lo,et al.  A heuristic error-feedback learning algorithm for fuzzy modeling , 1999, IEEE Trans. Syst. Man Cybern. Part A.

[30]  Chin-Teng Lin,et al.  An ART-based fuzzy adaptive learning control network , 1997, IEEE Trans. Fuzzy Syst..