کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10323117 | 660899 | 2005 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
POP-Yager: A novel self-organizing fuzzy neural network based on the Yager inference
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله

چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 29, Issue 1, July 2005, Pages 229-242
Journal: Expert Systems with Applications - Volume 29, Issue 1, July 2005, Pages 229-242
نویسندگان
C. Quek, A. Singh,