| Title |
The Hybrid Multi-layer Inference Architectures and Algorithms of FPNN Based on FNN and PNN |
| Authors |
박병준(Park, Byeong-Jun) ; 오성권(O, Seong-Gwon) ; 김현기(Kim, Hyeon-Gi) |
| Keywords |
퍼지 다항식 뉴럴네트워크 ; 퍼지 뉴럴네트워크 ; 다항식 뉴럴네트워크 GMDH (Group Method of Data Handling) ; BFPNN(Basic FPNN) ; MFPNN(Modified FPNN) ; Fuzzy Polynomial Neural Networks:FPNN ; Fuzzy Neural Network:FNN ; Polynomial Neural Networks:PNN |
| Abstract |
In this paper, we propose Fuzzy Polynomial Neural Networks(FPNN) based on Polynomial Neural Networks(PNN) and Fuzzy Neural Networks(FNN) for model identification of complex and nonlinear systems. The proposed FPNN is generated from the mutually combined structure of both FNN and PNN. The one and the other are considered as the premise part and consequence part of FPNN structure respectively. As the consequence part of FPNN, PNN is based on Group Method of Data Handling(GMDH) method and its structure is similar to Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and self-organizing networks that can be generated. FPNN is available effectively for multi-input variables and high-order polynomial according to the combination of FNN with PNN. Accordingly it is possible to consider the nonlinearity characteristics of process and to get better output performance with superb predictive ability. As the premise part of FPNN, FNN uses both the simplified fuzzy inference as fuzzy inference method and error back-propagation algorithm as learning rule. The parameters such as parameters of membership functions, learning rates and momentum coefficients are adjusted using genetic algorithms. And we use two kinds of FNN structure according to the division method of fuzzy space of input variables. One is basic FNN structure and uses fuzzy input space divided by each separated input variable, the other is modified FNN structure and uses fuzzy input space divided by mutually combined input variables. In order to evaluate the performance of proposed models, we use the nonlinear function and traffic route choice process. The results show that the proposed FPNN can produce the model with higher accuracy and more robustness than any other method presented previously. And also performance index related to the approximation and prediction capabilities of model is evaluated and discussed. |