Chaotic synchronisation for coupled neural networks based on T-S fuzzy theory

Gong, Dunwei; Liu, Jinhai; Zhao, Shuangyu

doi:10.1080/00207721.2013.795631

Cited by 9 publications

(3 citation statements)

References 29 publications

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“…By using Lemma 6 and introducing Finsler's Lemma, some delay-dependent conditions are acquired in complex networks. The criteria in this paper can be easily used in many existing references and obtain better results, such as [26][27][28][29][30].…”

Section: Remark 12mentioning

confidence: 99%

Pinning Synchronization for Complex Networks with Interval Coupling Delay by Variable Subintervals Method and Finsler’s Lemma

et al. 2017

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The pinning synchronous problem for complex networks with interval delays is studied in this paper. First, by using an inequality which is introduced from Newton-Leibniz formula, a new synchronization criterion is derived. Second, combining Finsler's Lemma with homogenous matrix, convergent linear matrix inequality (LMI) relaxations for synchronization analysis are proposed with matrix-valued coefficients. Third, a new variable subintervals method is applied to expand the obtained results. Different from previous results, the interval delays are divided into some subdelays, which can introduce more free weighting matrices. Fourth, the results are shown as LMI, which can be easily analyzed or tested. Finally, the stability of the networks is proved via Lyapunov's stability theorem, and the simulation of the trajectory claims the practicality of the proposed pinning control.

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Section: Remark 12mentioning

confidence: 99%

Pinning Synchronization for Complex Networks with Interval Coupling Delay by Variable Subintervals Method and Finsler’s Lemma

et al. 2017

Self Cite

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“…Recently, there is a rapidly growing interests of fuzzy systems, and many papers have investigated fuzzy systems, for example, [19,20] and so on. Especially, some researchers have paid their attention to TSF system [6,10,24], which is proposed by Takagi and Sugeno [18]. TSF system is always depicted by using some fuzzy IF-THEN rules, simply.…”

Section: Introductionmentioning

confidence: 99%

Fast fixed-time synchronization of T–S fuzzy complex networks

Liu

Zhao

Zhang

et al. 2021

NAMC

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In this paper, fast fixed-time (FDT) synchronization of T–S fuzzy (TSF) complex networks (CNs) is considered. The given control schemes can make the CNs synchronize with the given isolated system more fleetly than the most of existing results. By constructing comparison system and applying new analytical techniques, sufficient conditions are established to derive fast FDT synchronization speedily. In order to give some comparisons, FDT synchronization of the considered CNs is also presented by designing FDT fuzzy controller. Numerical examples are given to illustrate our new results.

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“…For the soft threshold, it is continuous but there is a deviation. Therefore, the fuzzy theory [17,18] is introduced to construct a fuzzy threshold function with a wavelet algorithm. Furthermore, the de-noising algorithm of wavelet fuzzy threshold is proposed, which can overcome potential disadvantages of the threshold function.…”

Section: Introductionmentioning

confidence: 99%

Night Vision Image De-noising of Apple Harvesting Robots Based on the Wavelet Fuzzy Threshold

Ruan

Zhao

Jia

et al. 2015

Int. j. adv. robot. syst.

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In this paper, the de-noising problem of night vision images is studied for apple harvesting robots working at night. The wavelet threshold method is applied to the de-noising of night vision images. Due to the fact that the choice of wavelet threshold function restricts the effect of the wavelet threshold method, the fuzzy theory is introduced to construct the fuzzy threshold function. We then propose the de-noising algorithm based on the wavelet fuzzy threshold. This new method can reduce image noise interferences, which is conducive to further image segmentation and recognition. To demonstrate the performance of the proposed method, we conducted simulation experiments and compared the median filtering and the wavelet soft threshold de-noising methods. It is shown that this new method can achieve the highest relative PSNR. Compared with the original images, the median filtering de-noising method and the classical wavelet threshold de-noising method, the relative PSNR increases 24.86%, 13.95%, and 11.38% respectively. We carry out comparisons from various aspects, such as intuitive visual evaluation, objective data evaluation, edge evaluation and artificial light evaluation. The experimental results show that the proposed method has unique advantages for the denoising of night vision images, which lay the foundation for apple harvesting robots working at night.

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Chaotic synchronisation for coupled neural networks based on T-S fuzzy theory

Cited by 9 publications

References 29 publications

Pinning Synchronization for Complex Networks with Interval Coupling Delay by Variable Subintervals Method and Finsler’s Lemma

Pinning Synchronization for Complex Networks with Interval Coupling Delay by Variable Subintervals Method and Finsler’s Lemma

Fast fixed-time synchronization of T–S fuzzy complex networks

Night Vision Image De-noising of Apple Harvesting Robots Based on the Wavelet Fuzzy Threshold

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