Chaotic Synchronization Using a Self-Evolving Recurrent Interval Type-2 Petri Cerebellar Model Articulation Controller

In this manuscript, the synchronization of four-dimensional (4D) chaotic systems with uncertain parameters using a self-evolving recurrent interval type-2 Petri cerebellar model articulation controller is studied. The design of the synchronization control system is comprised of a recurrent interval type-2 Petri cerebellar model articulation controller and a fuzzy compensation controller. The proposed network structure can automatically generate new rules or delete unnecessary rules based on the self-evolving algorithm. Furthermore, the gradient-descent method is applied to adjust the proposed network parameters. Through Lyapunov stability analysis, bounded system stability is guaranteed. Finally, the effectiveness of the proposed controller is illustrated using numerical simulations of 4D chaotic systems.

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Using Interval Type-2 Recurrent Fuzzy Cerebellar Model Articulation Controller Based on Improved Differential Evolution for Cooperative Carrying Controller of Mobile Robots

2020 International Conference on Fuzzy Theory and Its Applications (iFUZZY) ◽

10.1109/ifuzzy50310.2020.9297367 ◽

2020 ◽

Author(s):

Tzu-Chao Lin ◽

Chao-Chun Chen ◽

Cheng-Jian Lin

Keyword(s):

Differential Evolution ◽

Mobile Robots ◽

Cerebellar Model Articulation Controller ◽

Interval Type

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Synchronization of uncertain fractional-order hyper-chaotic systems via a novel adaptive interval type-2 fuzzy active sliding mode controller

International Journal of Dynamics and Control ◽

10.1007/s40435-015-0207-9 ◽

2015 ◽

Vol 5 (1) ◽

pp. 135-144 ◽

Cited By ~ 13

Author(s):

Milad Mohadeszadeh ◽

Hadi Delavari

Keyword(s):

Fractional Order ◽

Chaotic Systems ◽

Sliding Mode ◽

Sliding Mode Controller ◽

Interval Type

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System Identification Based on Dynamical Training for Recurrent Interval Type-2 Fuzzy Neural Network

Contemporary Theory and Pragmatic Approaches in Fuzzy Computing Utilization ◽

10.4018/978-1-4666-1870-1.ch013 ◽

2013 ◽

pp. 188-205

Author(s):

Tsung-Chih Lin ◽

Yi-Ming Chang ◽

Tun-Yuan Lee

Keyword(s):

Neural Network ◽

Neural Networks ◽

System Identification ◽

Nonlinear System ◽

Fuzzy Neural Networks ◽

Training Data ◽

Fuzzy Neural ◽

Interval Type ◽

Recurrent Interval

This paper proposes a novel fuzzy modeling approach for identification of dynamic systems. A fuzzy model, recurrent interval type-2 fuzzy neural network (RIT2FNN), is constructed by using a recurrent neural network which recurrent weights, mean and standard deviation of the membership functions are updated. The complete back propagation (BP) algorithm tuning equations used to tune the antecedent and consequent parameters for the interval type-2 fuzzy neural networks (IT2FNNs) are developed to handle the training data corrupted by noise or rule uncertainties for nonlinear system identification involving external disturbances. Only by using the current inputs and most recent outputs of the input layers, the system can be completely identified based on RIT2FNNs. In order to show that the interval IT2FNNs can handle the measurement uncertainties, training data are corrupted by white Gaussian noise with signal-to-noise ratio (SNR) 20 dB. Simulation results are obtained for the identification of nonlinear system, which yield more improved performance than those using recurrent type-1 fuzzy neural networks (RT1FNNs).

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