Synchronization of chaotic systems and identification of nonlinear systems by using recurrent hierarchical type-2 fuzzy neural networks.

This paper proposes a novel approach for training of proposed recurrent hierarchical interval type-2 fuzzy neural networks (RHT2FNN) based on the square-root cubature Kalman filters (SCKF). The SCKF algorithm is used to adjust the premise part of the type-2 FNN and the weights of defuzzification and the feedback weights. The recurrence property in the proposed network is the output feeding of each membership function to itself. The proposed RHT2FNN is employed in the sliding mode control scheme for the synchronization of chaotic systems. Unknown functions in the sliding mode control approach are estimated by RHT2FNN. Another application of the proposed RHT2FNN is the identification of dynamic nonlinear systems. The effectiveness of the proposed network and its learning algorithm is verified by several simulation examples. Furthermore, the universal approximation of RHT2FNNs is also shown.

[1]  Dragos Arotaritei,et al.  Genetic Algorithm for Fuzzy Neural Networks using Locally Crossover , 2011, Int. J. Comput. Commun. Control.

[2]  Chin-Teng Lin,et al.  An online self-constructing neural fuzzy inference network and its applications , 1998, IEEE Trans. Fuzzy Syst..

[3]  Chia-Feng Juang,et al.  Recurrent type-2 fuzzy neural network using Haar wavelet energy and entropy features for speech detection in noisy environments , 2012, Expert Syst. Appl..

[4]  Chia-Feng Juang,et al.  A TSK-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms , 2002, IEEE Trans. Fuzzy Syst..

[5]  Chia-Feng Juang,et al.  An Interval Type-2 Fuzzy-Neural Network With Support-Vector Regression for Noisy Regression Problems , 2010, IEEE Transactions on Fuzzy Systems.

[6]  Hani Hagras,et al.  A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots , 2004, IEEE Transactions on Fuzzy Systems.

[7]  Cheng-Jian Lin,et al.  Dynamic System Identification Using a Recurrent Compensatory Fuzzy Neural Network , 2008 .

[8]  Cuntai Guan,et al.  eT2FIS: An Evolving Type-2 Neural Fuzzy Inference System , 2013, Inf. Sci..

[9]  W. Rudin.  Principles of mathematical analysis , 1964 .

[10]  Jerry M. Mendel,et al.  Type-2 fuzzy logic systems , 1999, IEEE Trans. Fuzzy Syst..

[11]  Andreas S. Weigend,et al.  Time Series Prediction: Forecasting the Future and Understanding the Past , 1994 .

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

[13]  Ching-Hung Lee,et al.  Identification and control of dynamic systems using recurrent fuzzy neural networks , 2000, IEEE Trans. Fuzzy Syst..

[14]  Jie Zhang,et al.  Recurrent neuro-fuzzy networks for nonlinear process modeling , 1999, IEEE Trans. Neural Networks.

[15]  Chin-Teng Lin,et al.  Identification and Prediction of Dynamic Systems Using an Interactively Recurrent Self-Evolving Fuzzy Neural Network , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[16]  Chia-Feng Juang,et al.  A Self-Evolving Interval Type-2 Fuzzy Neural Network With Online Structure and Parameter Learning , 2008, IEEE Transactions on Fuzzy Systems.

[17]  S. Haykin,et al.  Cubature Kalman Filters , 2009, IEEE Transactions on Automatic Control.

[18]  Sameer Singh.  Noise impact on time-series forecasting using an intelligent pattern matching technique , 1999, Pattern Recognit..

[19]  Ying-Chung Wang,et al.  Direct adaptive iterative learning control of nonlinear systems using an output-recurrent fuzzy neural network , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[20]  Ching-Chih Tsai,et al.  Generalized predictive control using recurrent fuzzy neural networks for industrial processes , 2007 .

[21]  Rong-Jong Wai,et al.  Adaptive Moving-Target Tracking Control of a Vision-Based Mobile Robot via a Dynamic Petri Recurrent Fuzzy Neural Network , 2013, IEEE Transactions on Fuzzy Systems.

[22]  Rafik A. Aliev,et al.  Recurrent Fuzzy Neural Network Based System for Battery Charging , 2007, ISNN.

[23]  Dragos Arotaritei.  Recurrent algebraic fuzzy neural networks based on fuzzy numbers , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[25]  Yu-Ching Lin,et al.  Systems identification using type-2 fuzzy neural network (type-2 FNN) systems , 2003, Proceedings 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Computational Intelligence in Robotics and Automation for the New Millennium (Cat. No.03EX694).

[26]  Yang-Yin Lin,et al.  A Recurrent Self-Evolving Interval Type-2 Fuzzy Neural Network for Dynamic System Processing , 2009, IEEE Transactions on Fuzzy Systems.

[27]  Tsung-Chih Lin,et al.  Synchronization of uncertain chaotic systems based on adaptive type-2 fuzzy sliding mode control , 2011, Eng. Appl. Artif. Intell..

[28]  Mohammad Teshnehlab,et al.  Two-mode Indirect Adaptive Control Approach for the Synchronization of Uncertain Chaotic Systems by the Use of a Hierarchical Interval Type-2 Fuzzy Neural Network , 2014, IEEE Transactions on Fuzzy Systems.

[29]  Mojtaba Ahmadieh Khanesar,et al.  Extended Kalman Filter Based Learning Algorithm for Type-2 Fuzzy Logic Systems and Its Experimental Evaluation , 2012, IEEE Transactions on Industrial Electronics.

[30]  S. Han,et al.  Recurrent fuzzy neural network backstepping control for the prescribed output tracking performance of nonlinear dynamic systems. , 2014, ISA transactions.

[31]  Xiao-Jun Zeng,et al.  Learning for Hierarchical Fuzzy Systems Based on the Gradient-Descent Method , 2006, 2006 IEEE International Conference on Fuzzy Systems.