An Evolving Cascade System Based on A Set Of Neo Fuzzy Nodes

Neo-fuzzy elements are used as nodes for an evolving cascade system. The proposed system can tune both its parameters and architecture in an online mode. It can be used for solving a wide range of Data Mining tasks (namely time series forecasting). The evolving cascade system with neo-fuzzy nodes can process rather large data sets with high speed and effectiveness.

[1]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[2]  Leszek Rutkowski,et al.  Computational intelligence - methods and techniques , 2008 .

[3]  Paresh Chandra Deka,et al.  Modeling of Air Temperature using ANFIS by Wavelet Refined Parameters , 2016 .

[4]  Yevgeniy Bodyanskiy,et al.  EVOLVING CASCADE NEURAL NETWORK BASED ON MULTIDIMESNIONAL EPANECHNIKOV'S KERNELS AND ITS LEARNING ALGORITHM , 2011 .

[5]  Nikola Kasabov,et al.  Evolving Fuzzy Neural Networks : Theory and Applications for On-line Adaptive Prediction , Decision Making and Control , 1998 .

[6]  TSUTOMU MIKI Analog Implementation of Neo-Fuzzy Neuron and Its On-board Learning , 1999 .

[7]  Nikola Kasabov,et al.  Evolving connectionist systems , 2002 .

[8]  Illya Kokshenev,et al.  An adaptive learning algorithm for a neo fuzzy neuron , 2003, EUSFLAT Conf..

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

[10]  G. Pask,et al.  Heuristic Self-Organization in Problems of Engineering Cybernetics , 2003 .

[11]  Yevgeniy V. Bodyanskiy,et al.  A new learning algorithm for a forecasting neuro-fuzzy network , 2003, Integr. Comput. Aided Eng..

[12]  A. G. Ivakhnenko,et al.  Polynomial Theory of Complex Systems , 1971, IEEE Trans. Syst. Man Cybern..

[13]  Christian Borgelt,et al.  Computational Intelligence , 2016, Texts in Computer Science.

[14]  A. Ivakhnenko Heuristic self-organization in problems of engineering cybernetics , 1970 .

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

[16]  M.N.S. Swamy,et al.  Neural Networks and Statistical Learning , 2013 .

[17]  Jatinderkumar R. Saini,et al.  Estimation and Approximation Using Neuro- Fuzzy Systems , 2016 .

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

[19]  Takeshi Yamakawa,et al.  Soft Computing Based Signal Prediction, Restoration, and Filtering , 1997 .