Wavelet Interval Type-2 Fuzzy Quad-Function-Link Brain Emotional Control Algorithm for the Synchronization of 3D Nonlinear Chaotic Systems

Lin, Chih-Min; Lin, Chunshui; Le, Tien-Loc; Nguyen, Ngoc Phi; Hong, Sung-Kyung; Chao, Fei

doi:10.1007/s40815-020-00941-7

Cited by 25 publications

(15 citation statements)

References 46 publications

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“…[18][19][20][21] illustrate the comparison between the different controllers. It is clear that the tracking trajectories, the errors, and the control signals for the proposed SO-DFL-BELC achieve better performance than that for the conventional BELC [7], [8], the BELC updating and , the BELC using sliding surface, and the DFL-BELC. The RMSE result is also improved for the methods using the updating means and variances, the sliding surface for the updating rules, the DFL network, and the SO mechanism.…”

Section: Comments Of the Simulation Resultsmentioning

confidence: 95%

“…Normally, the reward function is chosen as the sum of the control signal and a function of the error of the system [31], [32]. Since the sliding surface is also a function of as (8), this paper proposes a novel reward function as (26).…”

Section: ) Neuron Increasing Processmentioning

confidence: 99%

“…Brain emotional learning controller (BELC) or brain emotional learning-based intelligent controller (BELBIC) is one of those methods. The idea is based on the research of Moren, Balkenius, and LeDoux that shows some parts of the brain produce emotion signals in the amygdala cortex and the orbitofrontal cortex [4][5][6][7][8]. From all of those studies, the structure of a BELC was built and utilized to control various systems such as doubly fed induction generator systems [9], omni-directional three-wheel robots [10], interior permanent-magnet synchronous motor drives [11], encoderless synchronous reluctance motor drives [12], etc.…”

Section: Introductionmentioning

confidence: 99%

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A New Self-Organizing Double Function-Link Brain Emotional Learning Controller for MIMO Nonlinear Systems Using Sliding Surface

Lin

Nguyen

2021

IEEE Access

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This paper aims to propose a new type of neural network which is the self-organizing double functionlink brain emotional learning controller (SO-DFL-BELC) for multiple input multiple output (MIMO) nonlinear systems. The proposed controller is a newly designed neural network containing the key mechanism of a typical brain emotional learning controller (BELC), which is a mathematical model that approximates the judgmental and emotional activity of a brain, in which it is combined with some additional functions and methods. Firstly, a double function-link (DFL) network is applied to expand the function for a BELC to improve the accuracy of the system weights. Secondly, the self-organizing mechanism is utilized to increase or decrease the number of neurons that possibly supports the main controller to adapt to the sharp change of the input and to reduce the computation time significantly. Thirdly, the learning rules of the SO-DFL-BELC are developed based on the gradient descent algorithm and sliding surface. Finally, all parameters of the system can be optimized. The proposed SO-DFL-BELC is applied to control two different MIMO nonlinear systems that are a 4D chaotic system and a four-tank system. The simulation results show the favorable control performance of the proposed control algorithm.

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Section: Comments Of the Simulation Resultsmentioning

confidence: 95%

Section: ) Neuron Increasing Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A New Self-Organizing Double Function-Link Brain Emotional Learning Controller for MIMO Nonlinear Systems Using Sliding Surface

Lin

Nguyen

2021

IEEE Access

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“…Fig. 25 (f), (h), and (j) show the recovered audio signal that can be fully restored on the receiver by using the RCMAC [34], FBELC [11], T2FBELC [13], WIT2FQFLBEC [31], and the proposed RCFBC, respectively. Figs.…”

Section: D) Mean Square Error (Mse) and Peak Signal-to-noise Ratio (Psnr)mentioning

confidence: 99%

“…Based on the above discussions, this study produces a new recurrent cerebellar fuzzy brain emotional learning controller (RCFBC). The proposed method is combined a BELC, a recurrent CMAC, and a fuzzy inference system to create the RCFBC that has the advantages of the recurrent CMAC [30] and the BELC [31] that are (1) Using external feedback via recurrent units, built-in loopback allows the network to remember the system's past states and to learn the system parameters. Based on this feature, recurrent CMAC usually shows quick response and good performance in the presence of uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Synchronization of Chaotic System Using a Brain-Imitated Neural Network Controller and Its Applications for Secure Communications

2021

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This paper proposes a new hybrid algorithm for secure communication applications. The proposed algorithm includes a fuzzy brain emotional learning controller (FBELC), a recurrent cerebellar model articulation controller (RCMAC), and a robust compensator (RC). The main brain-imitated neural network controller is a combination of the RCMAC and the FBELC, which is a mathematical model that approximates the decision and emotional activity of a human brain. A fuzzy inference system is also merged into the FBELC to produce an efficient hybrid structure, then it is used for secure communication applications. The 3-dimensional (3D) Genesio chaotic system is used for audio and image secure communication systems to show the potency and performance of the proposed algorithm. In the first application, a new image encryption algorithm is proposed to enhance security for information transmission, then several standard images are applied for the chaotic synchronization of image secure communication. In the second application, the audio signal is embedded in a 3D chaotic trajectory, which is used as an encryption carrier signal, after using the proposed method for the decryption, the source signal can be retrieved. The comparisons of simulation results using security analyses and root mean square error for recent algorithms are performed to validate the performance and efficiency of the proposed hybrid algorithm. The simulation results point out that our algorithm can attain better synchronization performance, and achieve more efficient audio and image secure communications.INDEX TERMS fuzzy inference system, brain emotional learning control, recurrent cerebellar model articulation controller, 3D chaotic systems, audio secure communication, image secure communication.

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Wavelet CMAC for MIMO uncertain nonlinear systems using a modified social ski‐driver algorithm

Lin

et al. 2022

Asian Journal of Control

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This study uses a Mexican hat wavelet membership function for a cerebellar model articulation controller (CMAC) to develop a more efficient adaptive controller for multiple input multiple output (MIMO) uncertain nonlinear systems. The main controller is called the adaptive Mexican hat wavelet CMAC (MWCMAC), and an auxiliary controller is used to remove the residual error. For the MWCMAC, the online learning laws are derived from the gradient descent method. In addition, the learning rate values are very important and have a great impact on the performance of the control system; however, they are difficult to choose accurately. Therefore, a modified social ski driver (SSD) algorithm is proposed to find optimal learning rates for the control parameters. Finally, a magnetic ball levitation system and a nine‐link biped robot are used to illustrate the effectiveness of the proposed SSD‐based MWCMAC control system. The comparisons with other existing control algorithms have shown the superiority of the proposed control system.

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Wavelet Interval Type-2 Fuzzy Quad-Function-Link Brain Emotional Control Algorithm for the Synchronization of 3D Nonlinear Chaotic Systems

Cited by 25 publications

References 46 publications

A New Self-Organizing Double Function-Link Brain Emotional Learning Controller for MIMO Nonlinear Systems Using Sliding Surface

A New Self-Organizing Double Function-Link Brain Emotional Learning Controller for MIMO Nonlinear Systems Using Sliding Surface

Synchronization of Chaotic System Using a Brain-Imitated Neural Network Controller and Its Applications for Secure Communications

Wavelet CMAC for MIMO uncertain nonlinear systems using a modified social ski‐driver algorithm

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