Have a personal or library account? Click to login
Intelligent financial time series forecasting: A complex neuro-fuzzy approach with multi-swarm intelligence Cover

Intelligent financial time series forecasting: A complex neuro-fuzzy approach with multi-swarm intelligence

International Journal of Applied Mathematics and Computer Science
Volume 22 (2012): Issue 4 (December 2012)
By:
Open Access
|Dec 2012

References

  1. Albus, J.S. (1975). Data storage in the cerebellar model articulation controller (CMAC), <em>Journal of Dynamic Systems, Measurement and Control </em><bold>97</bold>(2): 228-233.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1115/1.3426923" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1115/1.3426923</a></dgdoi:pub-id>
    Search in Google Scholar
  2. Boyacioglu, M.A. and Avci, D. (2010). An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: The case of the Istanbul stock exchange, <em>Expert Systems with Applications </em><bold>37</bold>(12): 7908-7912.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1016/j.eswa.2010.04.045" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.eswa.2010.04.045</a></dgdoi:pub-id>
    Search in Google Scholar
  3. Brdys´, M.A., Borowa, A., Idz´kowiak, P. and Brdys´, M.T. (2009). Adaptive prediction of stock exchange indices by state space wavelet networks, <em>International Journal of Applied Mathematics and Computer Science </em><bold>19</bold>(2): 337-348, DOI: <a href="https://doi.org/10.2478/v10006-009-0029-z." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.2478/v10006-009-0029-z.</a><dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.2478/v10006-009-0029-z" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.2478/v10006-009-0029-z</a></dgdoi:pub-id>
    Search in Google Scholar
  4. Buckley, J.J. (1989). Fuzzy complex numbers, <em>Fuzzy Sets and Systems </em><bold>33</bold>(3): 333-345.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1016/0165-0114(89)90122-X" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/0165-0114(89)90122-X</a></dgdoi:pub-id>
    Search in Google Scholar
  5. Castro, J.L. (1995). Fuzzy logic controllers are universal approximators, <em>IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans </em><bold>25</bold>(4): 629-635.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1109/21.370193" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1109/21.370193</a></dgdoi:pub-id>
    Search in Google Scholar
  6. Chen, Z., Aghakhani, S., Man, J. and Dick, S. (2011). ANCFIS: A neurofuzzy architecture employing complex fuzzy sets, <em>IEEE Transactions on Fuzzy Systems </em><bold>19</bold>(2): 305-322.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1109/TFUZZ.2010.2096469" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1109/TFUZZ.2010.2096469</a></dgdoi:pub-id>
    Search in Google Scholar
  7. Deng, X. and Wang, X. (2009). Incremental learning of dynamic fuzzy neural networks for accurate system modeling, <em>Fuzzy Sets and Systems </em><bold>160</bold>(7): 972-987.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1016/j.fss.2008.09.005" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.fss.2008.09.005</a></dgdoi:pub-id>
    Search in Google Scholar
  8. Dick, S. (2005). Toward complex fuzzy logic, <em>IEEE Transactions on Fuzzy Systems </em><bold>13</bold>(3): 405-414.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1109/TFUZZ.2004.839669" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1109/TFUZZ.2004.839669</a></dgdoi:pub-id>
    Search in Google Scholar
  9. Eberhart, R. and Kennedy, J. (1995). A new optimizer using particle swarm theory, <em>Proceedings of the 6th International Symposium on Micro Machine and Human Science, MHS 1995, Nagoya, Japan</em>, pp. 39-43.
    Search in Google Scholar
  10. Gao, Y. and Er, M.J. (2005). Narmax time series model prediction: Feedforward and recurrent fuzzy neural network approaches, <em>Fuzzy Sets and Systems </em><bold>150</bold>(2): 331-350.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1016/j.fss.2004.09.015" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.fss.2004.09.015</a></dgdoi:pub-id>
    Search in Google Scholar
  11. Graves, D. and Pedrycz, W. (2009). Fuzzy prediction architecture using recurrent neural networks, <em>Neurocom-puting </em><bold>72</bold>(7-9): 1668-1678.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1016/j.neucom.2008.07.009" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.neucom.2008.07.009</a></dgdoi:pub-id>
    Search in Google Scholar
  12. Hornik, K., Stinchcombe, M. and White, H. (1989). Multilayer feedforward networks are universal approximators, <em>Neural Networks </em><bold>2</bold>(5): 359-366.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1016/0893-6080(89)90020-8" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/0893-6080(89)90020-8</a></dgdoi:pub-id>
    Search in Google Scholar
  13. Jang, J.S.R. (1993). ANFIS: adaptive-network-based fuzzy inference system, <em>IEEE Transactions on Systems, Man, and Cybernetics </em><bold>23</bold>(3): 665-685.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1109/21.256541" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1109/21.256541</a></dgdoi:pub-id>
    Search in Google Scholar
  14. Kennedy, J. and Eberhart, R. (1995). Particle swarm optimization, <em>IEEE International Conference on Neural Networks, Perth, Australia</em>, pp. 1942-1948.
    Search in Google Scholar
  15. Khashei, M. and Bijari, M. (2011). A novel hybridization of artificial neural networks and ARIMA models for time series forecasting, <em>Applied Soft Computing </em><bold>11</bold>(2): 2664-2675.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1016/j.asoc.2010.10.015" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.asoc.2010.10.015</a></dgdoi:pub-id>
    Search in Google Scholar
  16. Kim, J. and Kasabov, N. (1999). HYFIS: Adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems, <em>Neural Networks </em><bold>12</bold>(9): 1301-1319.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1016/S0893-6080(99)00067-2" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/S0893-6080(99)00067-2</a></dgdoi:pub-id>
    Search in Google Scholar
  17. Li, C. and Cheng, H.-H. (2011). Intelligent forecasting of S&amp;P 500 time series-A self-organizing fuzzy approach, <em>in </em>N.T. Nguyen, C.-G. Kim and A. Janiak (Eds.), <em>Intelligent Information and Database Systems</em>, Lecture Notes in Artificial Intelligence, Vol. 6592, Springer-Verlag, Berlin/Heidelberg, pp. 411-420.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1007/978-3-642-20042-7_42" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1007/978-3-642-20042-7_42</a></dgdoi:pub-id>
    Search in Google Scholar
  18. Li, C. and Chiang, T.-W. (2011a). Complex fuzzy computing to time series prediction-A multi-swarm PSO learning approach, <em>in </em>N.T. Nguyen, C.-G. Kim and A. Janiak (Eds.) <em>Intelligent Information and Database Systems</em>, Lecture Notes in Artificial Intelligence, Vol. 6592, Springer-Verlag, Berlin/Heidelberg, pp. 242-251.
    Search in Google Scholar
  19. Li, C. and Chiang, T.-W. (2011b). Complex fuzzy model with PSO-RLSE hybrid learning approach to function approximation, <em>International Journal of Intelligent Information and Database Systems </em><bold>5</bold>(4): 409-430.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1504/IJIIDS.2011.041325" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1504/IJIIDS.2011.041325</a></dgdoi:pub-id>
    Search in Google Scholar
  20. Li, C. and Chiang, T.-W. (2011c). Function approximation with complex neuro-fuzzy system using complex fuzzy sets-A new approach, <em>New Generation Computing </em><bold>29</bold>(3): 261-276.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1007/s00354-011-0302-1" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1007/s00354-011-0302-1</a></dgdoi:pub-id>
    Search in Google Scholar
  21. Li, C., Chiang, T.-W., J.-W., H. and Wu, T. (2010). Complex neuro-fuzzy intelligent approach to function approximation, <em>3rd International Workshop on Advanced Computational Intelligence, IWACI 2010, Suzhou, China</em>, pp. 151-156.
    Search in Google Scholar
  22. Li, C. and Lee, C.-Y. (2003). Self-organizing neuro-fuzzy system for control of unknown plants, <em>IEEE Transactions on Fuzzy Systems </em><bold>11</bold>(1): 135-150.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1109/TFUZZ.2002.805898" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1109/TFUZZ.2002.805898</a></dgdoi:pub-id>
    Search in Google Scholar
  23. Li, C., Lee, C.-Y. and Cheng, K.-H. (2004). Pseudoerror-based self-organizing neuro-fuzzy system, <em>IEEE Transactions on Fuzzy Systems </em><bold>12</bold>(6): 812-819.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1109/TFUZZ.2004.836086" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1109/TFUZZ.2004.836086</a></dgdoi:pub-id>
    Search in Google Scholar
  24. Li, C. and Priemer, R. (1997). Self-learning general purpose PID controller, <em>Journal of the Franklin Institute </em><bold>334</bold>(2): 167-189.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1016/S0016-0032(97)81151-9" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/S0016-0032(97)81151-9</a></dgdoi:pub-id>
    Search in Google Scholar
  25. Li, C. and Priemer, R. (1999). Fuzzy control of unknown multiple-input-multiple-output plants, <em>Fuzzy Sets and Systems </em><bold>104</bold>(2): 245-267.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1016/S0165-0114(97)00217-0" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/S0165-0114(97)00217-0</a></dgdoi:pub-id>
    Search in Google Scholar
  26. Lu, C.-J., Lee, T.-S. and Chiu, C.-C. (2009). Financial time series forecasting using independent component analysis and support vector regression, <em>Decision Support Systems </em><bold>47</bold>(2): 115-125.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1016/j.dss.2009.02.001" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.dss.2009.02.001</a></dgdoi:pub-id>
    Search in Google Scholar
  27. Man, J.Y., Chen, Z. and Dick, S. (2007). Towards inductive learning of complex fuzzy inference systems, <em>Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS 2007, San Diego, CA, USA</em>, pp. 415-420.
    Search in Google Scholar
  28. Mansour, M.M., Mekhamer, S.F. and El-Kharbawe, N.-S. (2007). A modified particle swarm optimizer for the coordination of directional overcurrent relays, <em>IEEE Transactions on Power Delivery </em><bold>22</bold>(3): 1400-1410.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1109/TPWRD.2007.899259" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1109/TPWRD.2007.899259</a></dgdoi:pub-id>
    Search in Google Scholar
  29. Moody, J. and Darken, C.J. (1989). Fast learning in networks of locally-tuned processing units, <em>Neural Computation </em><bold>1</bold>(2): 281-294.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1162/neco.1989.1.2.281" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1162/neco.1989.1.2.281</a></dgdoi:pub-id>
    Search in Google Scholar
  30. Moses, D., Degani, O., Teodorescu, H.N., Friedman, M. and Kandel, A. (1999). Linguistic coordinate transformations for complex fuzzy sets, <em>IEEE International Fuzzy Systems Conference Proceedings, FUZZ-IEEE 1999, Seoul, Korea</em>, pp. 1340-1345.
    Search in Google Scholar
  31. Mousavi, S.J., Ponnambalam, K. and Karray, F. (2007). Inferring operating rules for reservoir operations using fuzzy regression and ANFIS, <em>Fuzzy Sets and Systems </em><bold>158</bold>(10): 1064-1082.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1016/j.fss.2006.10.024" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.fss.2006.10.024</a></dgdoi:pub-id>
    Search in Google Scholar
  32. Niu, B., Zhu, Y., He, X. and Wu, H. (2007). MCPSO: A multi-swarm cooperative particle swarm optimizer, <em>Applied Mathematics and Computation </em><bold>185</bold>(2): 1050-1062.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1016/j.amc.2006.07.026" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.amc.2006.07.026</a></dgdoi:pub-id>
    Search in Google Scholar
  33. Paul, S. and Kumar, S. (2002). Subsethood-product fuzzy neural inference system (SUPFUNIS), <em>IEEE Transactions on Neural Networks </em><bold>13</bold>(3): 578-599.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1109/TNN.2002.1000126" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1109/TNN.2002.1000126</a></dgdoi:pub-id><dgpm:pub-id xmlns:dgpm="http://degruyter.com/resources/fetched-pubmed-id" pub-id-type="pmid">18244458</dgpm:pub-id>
    Search in Google Scholar
  34. Ramot, D., Friedman, M., Langholz, G. and Kandel, A. (2003). Complex fuzzy logic, <em>IEEE Transactions on Fuzzy Systems </em><bold>11</bold>(4): 450-461.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1109/TFUZZ.2003.814832" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1109/TFUZZ.2003.814832</a></dgdoi:pub-id>
    Search in Google Scholar
  35. Ramot, D., Milo, R., Friedman, M. and Kandel, A. (2002). Complex fuzzy sets, <em>IEEE Transactions on Fuzzy Systems </em><bold>10</bold>(2): 171-186.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1109/91.995119" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1109/91.995119</a></dgdoi:pub-id>
    Search in Google Scholar
  36. Rojas, I., Valenzuela, O., Rojas, F., Guillen, A., Herrera, L.J., Pomares, H., Marquez, L. and Pasadas, M. (2008). Soft-computing techniques and ARMA model for time series prediction, <em>Neurocomputing </em><bold>71</bold>(4-6): 519-537.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1016/j.neucom.2007.07.018" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.neucom.2007.07.018</a></dgdoi:pub-id>
    Search in Google Scholar
  37. Simin´ski, K. (2010). Rule weights in a neuro-fuzzy system with a hierarchical domain partition, <em>International Journal of Applied Mathematics and Computer Science </em><bold>20</bold>(2): 337-347, DOI: <a href="https://doi.org/10.2478/v10006-010-0025-3." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.2478/v10006-010-0025-3.</a><dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.2478/v10006-010-0025-3" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.2478/v10006-010-0025-3</a></dgdoi:pub-id>
    Search in Google Scholar
  38. Smetek, M. and Trawinski, B. (2011). Selection of heterogeneous fuzzy model ensembles using self-adaptive genetic algorithms, <em>New Generation Computing </em><bold>29</bold>(3): 309-327.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1007/s00354-010-0305-3" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1007/s00354-010-0305-3</a></dgdoi:pub-id>
    Search in Google Scholar
  39. Tung, W.L. and Quek, C. (2011). Financial volatility trading using a self-organising neural-fuzzy semantic network and option straddle-based approach, <em>Expert Systems with Applications </em><bold>38</bold>(5): 4668-4688.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1016/j.eswa.2010.07.116" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.eswa.2010.07.116</a></dgdoi:pub-id><dgpm:pub-id xmlns:dgpm="http://degruyter.com/resources/fetched-pubmed-id" pub-id-type="pmcid">7126939</dgpm:pub-id><dgpm:pub-id xmlns:dgpm="http://degruyter.com/resources/fetched-pubmed-id" pub-id-type="pmid">32288336</dgpm:pub-id>
    Search in Google Scholar
  40. Vo, N., Quang, T., Dinh, T. and Dinh, T. (2011). Robust visual tracking using randomized forest and online appearance model, <em>in </em>N.T. Nguyen, C.-G. Kim and A. Janiak (Eds.), <em>Intelligent Information and Database Systems, </em>Lecture Notes in Artificial Intelligence, Vol. 6592, Springer-Verlag, Berlin/Heidelberg pp. 212-221.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1007/978-3-642-20042-7_22" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1007/978-3-642-20042-7_22</a></dgdoi:pub-id>
    Search in Google Scholar
  41. Yahoo Finance for Hang Seng Index (2011). Website: http://finance.yahoo.com/q?s="HSI.
    Search in Google Scholar
  42. Yahoo Finance for Nikkei 225 Index (2011). Website, http://finance.yahoo.com/q?s="N225.
    Search in Google Scholar
  43. Yahoo Finance for Taiwan Stock Exchange Capitalization Weighted Stock Index (2011). Website, http://finance.yahoo.com/q?s="TWII.
    Search in Google Scholar
  44. Yuhui, S. and Eberhart, R.C. (2001). Fuzzy adaptive particle swarm optimization, <em>Proceedings of the 2001 Congress on Evolutionary Computation, Seoul, Korea, </em>pp. 101-106.
    Search in Google Scholar
  45. Zhang, G., Dillon, T.S., Cai, K.-Y, Ma, J. and Lu, J. (2009). Operation properties and (δ-equalities of complex fuzzy sets, <em>International Journal of Approximate Reasoning </em><bold>50</bold>(8): 1227-1249.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1016/j.ijar.2009.05.010" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.ijar.2009.05.010</a></dgdoi:pub-id>
    Search in Google Scholar
DOI: https://doi.org/10.2478/v10006-012-0058-x | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
Journal RSS Feed
Language: English
Page range: 787 - 800
Published on: Dec 28, 2012
Published by: Sciendo
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year
Keywords:
complex fuzzy set,
complex neuro-fuzzy system,
hierarchical multi-swarm particle swarm optimization,
recursive least squares estimator,
time series forecasting.
Related subjects:
Mathematics,
Applied mathematics

© 2012 Chunshien Li, Tai-Wei Chiang, published by Sciendo
This work is licensed under the Creative Commons License.

Volume 22 (2012): Issue 4 (December 2012)Next article