ANCFIS: A Neurofuzzy Architecture Employing Complex Fuzzy Sets. Complex fuzzy sets (CFSs) are an extension of type-1 fuzzy sets in which the membership of an object to the set is a value from the unit disc of the complex plane. Although there has been considerable progress made in determining the properties of CFSs and complex fuzzy logic, there has yet to be any practical application of this concept. We present the adaptive neurocomplex-fuzzy-inferential system (ANCFIS), which is the first neurofuzzy system architecture to implement complex fuzzy rules (and, in particular, the signature property of rule interference). We have applied this neurofuzzy system to the domain of time-series forecasting, which is an important machine-learning problem. We find that ANCFIS performs well in one synthetic and five real-world forecasting problems and is also very parsimonious. Experimental comparisons show that ANCFIS is comparable with existing approaches on our five datasets. This work demonstrates the utility of complex fuzzy logic on real-world problems.

References in zbMATH (referenced in 11 articles )

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  1. Mahmood, Tahir; ur Rehman, Ubaid: A method to multi-attribute decision making technique based on Dombi aggregation operators under bipolar complex fuzzy information (2022)
  2. Chen, Ying; Bi, Lvqing; Hu, Bo; Dai, Songsong: General complex-valued overlap functions (2021)
  3. Garg, Harish; Ali, Zeeshan; Gwak, Jeonghwan; Mahmood, Tahir; Aljahdali, Sultan: Some complex intuitionistic uncertain linguistic heronian mean operators and their application in multiattribute group decision making (2021)
  4. Imtiaz, Aneeza; Shuaib, Umer; Alolaiyan, Hanan; Razaq, Abdul; Gulistan, Muhammad: On structural properties of (\xi)-complex fuzzy sets and their applications (2020)
  5. Yazdanbakhsh, Omolbanin; Dick, Scott: FANCFIS: fast adaptive neuro-complex fuzzy inference system (2019)
  6. Liu, Yan; Yang, Dakun; Li, Feng: Smoothed (L_1/2) regularizer learning for split-complex valued neuro-fuzzy algorithm for TSK system and its convergence results (2018)
  7. Yazdanbakhsh, Omolbanin; Dick, Scott: A systematic review of complex fuzzy sets and logic (2018)
  8. Yazdanbakhsh, Omolbanin; Dick, Scott: Time-series forecasting via complex fuzzy logic (2015)
  9. Karpenko, Daria; Van Gorder, Robert A.; Kandel, Abraham: The Cauchy problem for complex fuzzy differential equations (2014)
  10. Alkouri, Abd Ulazeez M.; Salleh, Abdul Razak: Complex Atanassov’s intuitionistic fuzzy relation (2013)
  11. Li, Chunshien; Chiang, Tai-Wei: Intelligent financial time series forecasting: a complex neuro-fuzzy approach with multi-swarm intelligence (2012)