A Hybrid Cascade Neuro–Fuzzy Network with Pools of Extended Neo–Fuzzy Neurons and its Deep Learning

Abstract This research contribution instantiates a framework of a hybrid cascade neural network based on the application of a specific sort of neo-fuzzy elements and a new peculiar adaptive training rule. The main trait of the offered system is its competence to continue intensifying its cascades until the required accuracy is gained. A distinctive rapid training procedure is also covered for this case that offers the possibility to operate with non-stationary data streams in an attempt to provide online training of multiple parametric variables. A new training criterion is examined for handling non-stationary objects. Additionally, there is always an occasion to set up (increase) the inference order and the number of membership relations inside the extended neo-fuzzy neuron.

[1]  Oleksii K. Tyshchenko,et al.  An Evolving Cascade System Based on A Set Of Neo Fuzzy Nodes , 2016, ArXiv.

[2]  H. Uzawa,et al.  Preference, production, and capital: Iterative methods for concave programming , 1989 .

[3]  Dursun Delen,et al.  Real-World Data Mining: Applied Business Analytics and Decision Making , 2014 .

[4]  Fernando Gomide,et al.  Evolving Neo-fuzzy Neural Network with Adaptive Feature Selection , 2013, 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence.

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

[6]  Simon Haykin,et al.  Neural Networks and Learning Machines , 2010 .

[7]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .

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

[9]  Walmir M. Caminhas,et al.  Multivariable Gaussian Evolving Fuzzy Modeling System , 2011, IEEE Transactions on Fuzzy Systems.

[10]  Jesús S. Aguilar-Ruiz,et al.  Knowledge discovery from data streams , 2009, Intell. Data Anal..

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

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

[13]  Oleksii K. Tyshchenko,et al.  An evolving radial basis neural network with adaptive learning of its parameters and architecture , 2015, Automatic Control and Computer Sciences.

[14]  Donald K. Wedding,et al.  Discovering Knowledge in Data, an Introduction to Data Mining , 2005, Inf. Process. Manag..

[15]  L X Wang,et al.  Fuzzy basis functions, universal approximation, and orthogonal least-squares learning , 1992, IEEE Trans. Neural Networks.

[16]  Oleksii K. Tyshchenko,et al.  Adaptive learning of an evolving cascade neo-fuzzy system in data stream mining tasks , 2016, Evol. Syst..

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

[18]  Christian Lebiere,et al.  The Cascade-Correlation Learning Architecture , 1989, NIPS.

[19]  Krzysztof Krawiec,et al.  Exploring complex and big data , 2017, Int. J. Appl. Math. Comput. Sci..

[20]  Oleksii K. Tyshchenko,et al.  A deep cascade neural network based on extended neo-fuzzy neurons and its adaptive learning algorithm , 2017, 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON).

[21]  Maciej Jaworski,et al.  Regression Function and Noise Variance Tracking Methods for Data Streams with Concept Drift , 2018, Int. J. Appl. Math. Comput. Sci..

[22]  Charu C. Aggarwal,et al.  Data Mining: The Textbook , 2015 .

[23]  Oleksii K. Tyshchenko,et al.  A Hybrid Cascade Neural Network with Ensembles of Extended Neo-Fuzzy Neurons and Its Deep Learning , 2018, Advances in Intelligent Systems and Computing.

[24]  Oleksii K. Tyshchenko,et al.  A hybrid cascade neural network with an optimized pool in each cascade , 2015, Soft Comput..

[25]  Grady Hanrahan,et al.  Artificial Neural Networks in Biological and Environmental Analysis , 2011 .

[26]  Frank Klawonn,et al.  Computational Intelligence: A Methodological Introduction , 2015, Texts in Computer Science.