POP-Yager: A novel self-organizing fuzzy neural network based on the Yager inference

  • Authors:

  • C. Quek;A. Singh

  • Affiliations:

  • Intelligent Systems Laboratory, Blk N4 #2A-32, Nanyang Avenue, Nanyang Technological University, Singapore, Singapore 639798;Intelligent Systems Laboratory, Blk N4 #2A-32, Nanyang Avenue, Nanyang Technological University, Singapore, Singapore 639798

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2005

Quantified Score

Hi-index 12.07

Visualization

Abstract

A Pseudo-Outer Product based Fuzzy neural network using the Yager Rule of Inference [Keller, J. M., Yager, R. R., & Tahani, H. (1992). Neural network implementation of fuzzy logic. Fuzzy Sets and Systems, 45(1), 1-12.] called the POP-Yager FNN is proposed in this paper. The proposed POP-Yager FNN training consists of two phases; namely: the fuzzy membership derivation phase using the Modified Learning Vector Quantization (MLVQ) method [Ang, K. K. (1998). POPFNN-CRI(S): A Fuzzy neural network based on the compositional rule of inference. M. Phil. Dissertation, Nanyang Technological University.]; and the rule identification phase using the novel one-pass LazyPOP learning algorithm [Quek, C. & Zhou, R. W. (1999). The POP learning algorithms: Reducing work in identifying fuzzy rules. Neural Networks, 14(10), 1431-1445]. The proposed two-phase learning process effectively constructs the membership functions and identifies the fuzzy rules. Inference process in POP-Yager is based on the well-established Yager fuzzy inference rule [Keller, J. M., Yager, R. R. & Tahani, H. (1992). Neural network implementation of fuzzy logic. Fuzzy Sets and Systems, 45(1), 1-12]. Operations in POP-Yager strictly perform the logical processes of the Yager inference rule. This gives the novel network a strong theoretical foundation. Extensive experimental results based on the classification performance of the POP-Yager FNN using the Anderson's Iris data [Duda, R. O. & Hart, P. E. (1973). Pattern classification and scene analysis. Wiley.] are presented for discussion. Results show that the POP-Yager FNN possesses excellent recall and generalization abilities. The performance of the POP-Yager FNN as a general approximation of traffic flow data [Tan, G. K. (1997). Feasibility of predicting congestion states with neural networks. Final Year Project Report, Nanyang Technological University, CSE.] is analysed.