Chaos Synchronization Using Brain-Emotional-Learning-Based Fuzzy Control

Hsu, Chun‐Fei; Su, Chien-Ting; Lee, Tsu‐Tian

doi:10.1109/scis-isis.2016.0175

Cited by 11 publications

(6 citation statements)

References 23 publications

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“…For that reason, BELN still works well with system uncertainty with fast learning speeds and good approximation capabilities, and it can reduce tracking errors effectively. Over the years, some remarkable studies have applied BELN in different fields (Dashti et al 2017;Hsu et al 2016;Kong et al 2019;Le et al 2018).…”

Section: Interval Type-2 Fuzzy Brain Emotional Control Design For the Synchronization Of 4d Nonlinear Hyperchaotic Systemsmentioning

confidence: 99%

Interval type-2 fuzzy brain emotional control design for the synchronization of 4D nonlinear hyperchaotic systems

Lin

et al. 2021

Soft Comput

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This research provides a novel intelligent control structure for 4D nonlinear hyperchaotic systems. This is a hybrid design containing a new interval type-2 fuzzy fourfold function-link brain emotional controller and a smooth robust controller. It comprises a fuzzy inference system and three subnetworks. The subnetworks are a new fourfold function-link network, a type-2 fuzzy amygdala network and a type-2 fuzzy prefrontal cortex network that decrease the synchronization errors efficiently, follow the reference signal well and achieve good performance. Two Lyapunov stability functions are utilized to get the adaptive laws, and they are applied to online tune the parameters of the system. The proposed design is used to synchronize two 4D nonlinear hyperchaotic systems and the simulation results are given to demonstrate its superiority and effectiveness.

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Section: Interval Type-2 Fuzzy Brain Emotional Control Design For the Synchronization Of 4d Nonlinear Hyperchaotic Systemsmentioning

confidence: 99%

Interval type-2 fuzzy brain emotional control design for the synchronization of 4D nonlinear hyperchaotic systems

Lin

et al. 2021

Soft Comput

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“…Exploring the prior researches, there are different formulas used for developing the reward signals. If the network is applied to control system, the reward signal is always connected with control error, frequency deviation [36], or some other control signals, such as sliding-mode control signal [37]. Likewise, for the classification problem, it is important to define this reward signal properly.…”

Section: The Learning Algorithm For Fbelcmentioning

confidence: 99%

A Fuzzy Brain Emotional Learning Classifier Design and Application in Medical Diagnosis

2019

APH

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This paper aims to propose a fuzzy brain emotional learning classifier and applies it to medical diagnosis. To improve the generalization and learning ability, this classifier is combined with a fuzzy inference system and a brain emotional learning model. Meanwhile, different from a brain emotional learning controller, a novel definition of the reward signal is developed, which is more suitable for classification. In addition, a stable convergence is guaranteed by utilizing the Lyapunov stability theorem. Finally, the proposed method is applied for the leukemia classification and the diagnosis of heart disease. A comparison between the proposed method with other algorithms shows that this proposed classifier can be viewed as an efficient way to implement medical decision and diagnosis.

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“…Compared to other neural networks, the BEL network could achieve faster learning speed and better approximation ability for its special structure, which means that the BEL has two parts including an orbitofrontal cortex and an amygdala that are responsible for human emotional sensing and analyzing. us, in recent years, the BEL has been used for various fields such as prediction, identification, and control [25][26][27][28][29][30][31][32][33]. Meanwhile, the fuzzy neural network (FNN) takes advantages of both fuzzy logic and neural networks.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Control Based on the Interval Type-2 Intuition Fuzzy Brain Emotional Learning Network for the Multiple Degree-of-Freedom Rehabilitation Robot

Chen

et al. 2021

Mathematical Problems in Engineering

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A novel interval type-2 intuition fuzzy brain emotional learning network model (IT2IFBELC) which depends only on the input and output data is proposed for the rehabilitation robot, which is different from model-based control algorithms that require exact dynamic model knowledge of the rehabilitation robot. The proposed model takes advantage of the type-2 intuition fuzzy theory and brain emotional neural network, and this is no rule initially; then, the structure and parameters of IT2IFBELC are tuned online simultaneously by the gradient approach and Lyapunov function. The system input data streams are directly imported into the neural network through an interval type-2 intuition fuzzy inference system (IT2IFIS), and then the results are subsequently piped into sensory and emotional channels which jointly produce the final outputs of the network. That is, the whole controller is composed of three parts, including the ideal sliding mode controller, the interval type-2 intuition fuzzy brain emotional learning network controller, and a powerful robust compensation controller, and then one Lyapunov function is designed to guarantee the rapid convergence of the control systems. For further illustrating the superiority of this model, several models are studied here for comparison, and the results show that the interval type-2 intuition fuzzy brain emotional learning network model can obtain better satisfactory control performance and be suitable to deal with the influence of the uncertainty of the rehabilitation robot.

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Chaos Synchronization Using Brain-Emotional-Learning-Based Fuzzy Control

Cited by 11 publications

References 23 publications

Interval type-2 fuzzy brain emotional control design for the synchronization of 4D nonlinear hyperchaotic systems

Interval type-2 fuzzy brain emotional control design for the synchronization of 4D nonlinear hyperchaotic systems

A Fuzzy Brain Emotional Learning Classifier Design and Application in Medical Diagnosis

Data-Driven Control Based on the Interval Type-2 Intuition Fuzzy Brain Emotional Learning Network for the Multiple Degree-of-Freedom Rehabilitation Robot

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