Data Encryption and Transmission Based on Personal ECG Signals

Chen, Ching-Kun; Lin, Chun-Liang; Lin, Shyan-Lung; Chiang, Cheng-Tang

doi:10.4172/2090-4886.1000124

Cited by 3 publications

(1 citation statement)

References 41 publications

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“…This new modeling approach was proposed in an attempt to raise the efficiency of fuzzy modeling, simplify the modeling procedure, and reduce the numbers of modeling materials and feedback control inputs during fuzzy control. This novel modeling approach has been cited over 50 times in various research fields and applications; for instance, H∞ control design [38][39][40][41][42], design and control of memristive systems [43][44][45][46], data encryption [47][48][49][50], sampled-data control [51][52][53] and other applications [54][55][56][57][58][59][60]. Moreover, Li et al [61] extended a new concept of a nonlinear terms group to further improve the effectiveness of the G-L fuzzy model in 2015, called advanced G-L fuzzy modeling strategy (GLT fuzzy system).…”

Section: Introductionmentioning

confidence: 99%

Adaptive Control of Advanced G-L Fuzzy Systems with Several Uncertain Terms in Membership-Matrices

Tsai

Chen

et al. 2022

Processes

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In this paper, a set of novel adaptive control strategies based on an advanced G-L (proposed by Ge-Li-Tam, called GLT) fuzzy system is proposed. Three main design points can be summarized as follows: (1) the unknown parameters in a nonlinear dynamic system are regarded as extra nonlinear terms and are further packaged into so-called nonlinear terms groups for each equation through the modeling process, which reduces the complexity of the GLT fuzzy system; (2) the error dynamics are further rearranged into two parts, adjustable membership function and uncertain membership function, to aid the design of the controllers; (3) a set of adaptive controllers change with the estimated parameters and the update laws of parameters are provided following the current form of error dynamics. Two identical nonlinear dynamic systems based on a Quantum-CNN system (Q-CNN system) with two added terms are employed for simulations to demonstrate the feasibility as well as the effectiveness of the proposed fuzzy adaptive control scheme, where the tracking error can be eliminated efficiently.

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