Evolving GMDH-neuro-fuzzy system with small number of tuning parameters

In the paper a new class of evolving fuzzy networks is suggested, namely the evolving GMDH-neuro-fuzzy systems (Group Method of Data Handling). Their main advantage is a small number of tuning parameters that simplifies the training algorithms and cut training time comparing with classical evolving fuzzy neural networks (EFNN). The architecture and training algorithms for this neural network are considered. The experimental investigations of the proposed GMDH-neuro-fuzzy systems were carried out in the problem of stock prices forecasting and comparisons with GMDH algorithm and the cascade neuro-fuzzy network were performed that enable their efficiency estimation.

[1]  Duc Truong Pham,et al.  Neural Networks for Identification, Prediction and Control , 1995 .

[2]  Yevgeniy Bodyanskiy,et al.  Self-Learning Cascade Spiking Neural Network for Fuzzy Clustering Based on Group Method of Data Handling , 2013 .

[3]  Plamen Angelov,et al.  Evolving Rule-Based Models: A Tool For Design Of Flexible Adaptive Systems , 2002 .

[4]  A. G. Ivakhnenko,et al.  Inductive sorting-out GMDH algorithms with polynomial complexity for active neurons of neural network , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[5]  Chuen-Tsai Sun,et al.  Neuro-fuzzy And Soft Computing: A Computational Approach To Learning And Machine Intelligence [Books in Brief] , 1997, IEEE Transactions on Neural Networks.

[6]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[7]  M. Sugeno,et al.  Structure identification of fuzzy model , 1988 .

[8]  Tadashi Kondo.  Identification of radial basis function networks by using revised GMDH-type neural networks with a feedback loop , 2002, Proceedings of the 41st SICE Annual Conference. SICE 2002..

[9]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[10]  Yakov Frayman,et al.  A dynamically-constructed fuzzy neural controller for direct model reference adaptive control of multi-input-multi-output nonlinear processes , 2002, Soft Comput..

[11]  Edwin Lughofer,et al.  Evolving Fuzzy Systems - Methodologies, Advanced Concepts and Applications , 2011, Studies in Fuzziness and Soft Computing.

[12]  Takashi Ohtani,et al.  Automatic variable selection in RBF network and its application to neurofuzzy GMDH , 2000, KES'2000. Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies. Proceedings (Cat. No.00TH8516).

[13]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[14]  Yakov Frayman,et al.  Data Mining Using Dynamically Constructed Recurrent Fuzzy Neural Networks , 1998, PAKDD.

[15]  Li-Xin Wang,et al.  Adaptive fuzzy systems and control , 1994 .