An Extended Neo-Fuzzy Neuron and its Adaptive Learning Algorithm

A modification of the neo-fuzzy neuron is proposed (an extended neo-fuzzy neuron (ENFN)) that is characterized by improved approximating properties. An adaptive learning algorithm is proposed that has both tracking and smoothing properties. An ENFN distinctive feature is its computational simplicity compared to other artificial neural networks and neuro-fuzzy systems.

[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]  Daniel Graupe,et al.  Principles of Artificial Neural Networks , 2018, Advanced Series in Circuits and Systems.

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

[4]  Kenji Suzuki,et al.  Artificial Neural Networks: Architectures and Applications , 2014 .

[5]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

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

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

[8]  Peter Pd Dr.-Ing. habil. Otto,et al.  A self-tuning robust neo-fuzzy controller with constraints on control actions , 2010 .

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

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

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

[12]  L. Glass,et al.  Oscillation and chaos in physiological control systems. , 1977, Science.

[13]  K S Narendra,et al.  IDENTIFICATION AND CONTROL OF DYNAMIC SYSTEMS USING NEURAL NETWORKS , 1990 .

[14]  Jianwei Zhang,et al.  Constructing fuzzy controllers with B-spline models - Principles and applications , 1998, Int. J. Intell. Syst..

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

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

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

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

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

[20]  P. Ramadge,et al.  Discrete time stochastic adaptive control , 1979, 1979 18th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes.

[21]  M. MendelJ.,et al.  Fuzzy basis functions , 1995 .