Abstract
A new complex fuzzy computing paradigm using complex fuzzy sets (CFSs) to the problem of time series forecasting is proposed in this study. Distinctive from traditional type-1 fuzzy set, the membership for elements belong to a CFS is characterized in the unit disc of the complex plane. Based on the property of complex-valued membership, CFSs can be used to design a neural fuzzy system so that the system can have excellent adaptive ability. The proposed system is called the complex neuro-fuzzy system (CNFS). To update the free parameters of the CNFS, we devise a novel hybrid HMSPSO-RLSE learning method. The HMSPSO is a multi-swarm-based optimization method, first devised by us, and it is used to adjust the premise parameters of the CNFS. The RLSE is used to update the consequent parameters. Two examples for time series foresting are used to test the proposed approach. Through the experimental results, excellent performance has been exposed.
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References
Jang, S.R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics 23, 665–685 (1993)
Herrera, L.J., Pomares, H., Rojas, I., Guillén, A., González, J., Awad, M., Herrera, A.: Multigrid-based fuzzy systems for time series prediction: CATS competition. Neurocomputing 70, 2410–2425 (2007)
Boyacioglu, M.A., Avci, D.: An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange. Expert Systems with Applications 37, 7908–7912 (2010)
Deng, X., Wang, X.: Incremental learning of dynamic fuzzy neural networks for accurate system modeling. Fuzzy Sets and Systems 160, 972–987 (2009)
Mousavi, S.J., Ponnambalam, K., Karray, F.: Inferring operating rules for reservoir operations using fuzzy regression and ANFIS. Fuzzy Sets and Systems 158, 1064–1082 (2007)
Ramot, D., Milo, R., Friedman, M., Kandel, A.: Complex fuzzy sets. IEEE Transactions on Fuzzy Systems 10, 171–186 (2002)
Dick, S.: Toward complex fuzzy logic. IEEE Transactions on Fuzzy Systems 13, 405–414 (2005)
Moses, D., Degani, O., Teodorescu, H.N., Friedman, M., Kandel, A.: Linguistic coordinate transformations for complex fuzzy sets. Fuzzy Systems Conference Proceedings 3, 1340–1345 (1999)
Ramot, D., Milo, R., Friedman, M., Kandel, A.: Complex fuzzy logic. IEEE Transactions on Fuzzy Systems 11, 450–461 (2003)
Niu, B., Zhu, Y., He, X., Wu, H.: MCPSO: A multi-swarm cooperative particle swarm optimizer. Applied Mathematics and Computation 185, 1050–1062 (2007)
Graves, D., Pedrycz, W.: Fuzzy prediction architecture using recurrent neural networks. Neurocomputing 72, 1668–1678 (2009)
Castro, J.L.: Fuzzy logic controllers are universal approximators. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 25, 629–635 (1995)
Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Networks 2, 359–366 (1989)
Buckley, J.J.: Fuzzy complex numbers. Fuzzy Sets and Systems 33, 333–345 (1989)
Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science (1995)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks Proceedings (1995)
Yuhui, S., Eberhart, R.C.: Fuzzy adaptive particle swarm optimization. In: Proceedings of the 2001 Congress on Evolutionary Computation (2001)
Mansour, M.M., Mekhamer, S.F., El-Kharbawe, N.E.S.: A Modified Particle Swarm Optimizer for the Coordination of Directional Overcurrent Relays. IEEE Transactions on Power Delivery 22, 1400–1410 (2007)
Time Series Data Library, Physics, Daily brightness of a variable star, http://www-personal.buseco.monash.edu.au/hyndman/TSDL/S
Time Series Data Library, Micro-Economics, Oil prices in constant 1997 dollars, http://www-personal.buseco.monash.edu.au/hyndman/TSDL/S
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Li, C., Chiang, TW. (2011). Complex Fuzzy Computing to Time Series Prediction — A Multi-Swarm PSO Learning Approach. In: Nguyen, N.T., Kim, CG., Janiak, A. (eds) Intelligent Information and Database Systems. ACIIDS 2011. Lecture Notes in Computer Science(), vol 6592. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20042-7_25
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DOI: https://doi.org/10.1007/978-3-642-20042-7_25
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