GenSoFNN: a generic self-organizing fuzzy neural network. Existing neural fuzzy (neuro-fuzzy) networks proposed in the literature can be broadly classified into two groups. The first group is essentially fuzzy systems with self-tuning capabilities and requires an initial rule base to be specified prior to training. The second group of neural fuzzy networks, on the other hand, is able to automatically formulate the fuzzy rules from the numerical training data. No initial rule base needs to be specified prior to training. A cluster analysis is first performed on the training data and the fuzzy rules are subsequently derived through the proper connections of these computed clusters. However, most existing neural fuzzy systems (whether they belong to the first or second group) encountered one or more of the following major problems. They are (1) inconsistent rule-base; (2) heuristically defined node operations; (3) susceptibility to noisy training data and the stability-plasticity dilemma; and (4) needs for prior knowledge such as the number of clusters to be computed. Hence, a novel neural fuzzy system that is immune to the above-mentioned deficiencies is proposed in this paper. This new neural fuzzy system is named the generic self-organizing fuzzy neural network (GenSoFNN). The GenSoFNN network has strong noise tolerance capability by employing a new clustering technique known as discrete incremental clustering (DIC). The fuzzy rule base of the GenSoFNN network is consistent and compact as GenSoFNN has built-in mechanisms to identify and prune redundant and/or obsolete rules. Extensive simulations were conducted using the proposed GenSoFNN network and its performance is encouraging when benchmarked against other neural and neural fuzzy systems

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  1. Wu, Ailong; Zeng, Zhigang: Dynamic behaviors of memristor-based recurrent neural networks with time-varying delays (2012)
  2. Bahrammirzaee, Arash: A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems (2010) ioport
  3. Lee, Yonggon; Hui, Stefen; Zivi, Edwin; Żak, Stanislaw H.: Variable neural adaptive robust controllers for uncertain systems (2008)
  4. Lin, Cheng-Jian: An efficient immune-based symbiotic particle swarm optimization learning algorithm for TSK-type neuro-fuzzy networks design (2008) ioport
  5. Tan, A.; Quek, C.; Yow, K. C.: Maximizing winning trades using a novel RSPOP fuzzy neural network intelligent stock trading system (2008) ioport
  6. Lee, Chi-Yung; Lin, Cheng-Jian; Chen, Huei-Jen: A self-constructing fuzzy CMAC model and its applications (2007)
  7. Tung, W. L.; Quek, C.: A brain-inspired fuzzy semantic memory model for learning and reasoning with uncertainty (2007) ioport
  8. Lin, Cheng-Jian; Xu, Yong-Ji: A self-adaptive neural fuzzy network with group-based symbiotic evolution and its prediction applications (2006)
  9. Tung, W. L.; Quek, C.; Cheng, P.: GenSo-EWS: a novel neural-fuzzy based early warning system for predicting bank failures (2004) ioport