A Switched Operation Approach to Sampled-Data Control Stabilization of Fuzzy Memristive Neural Networks With Time-Varying Delay

Wang, Xin; Park, Ju H.; Zhong, Shouming; Yang, Huilan

doi:10.1109/tnnls.2019.2910574

Cited by 67 publications

(13 citation statements)

References 51 publications

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“…Due to the duet structure of Matouqin, in the volume processing of splitting in each sound part, each sound part can be played as P (weak), and the two sound parts are strong at the same time until the fifteenth bar is f (strong). In the strong position, the two sound parts are split into MF (medium strong) [9]. Carpenter and others believe that this strong and weak segmentation method stems from the different prominence of strong and weak tones.…”

Section: Literature Reviewmentioning

confidence: 99%

Pitch Evaluation of Matouqin Chamber Music Performance Based on Artificial Neural Network

2022

Mobile Information Systems

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In order to study the pitch evaluation of Matouqin chamber music performance based on artificial neural network, this paper puts forward the relevant theories in the fields of human ear auditory perception system, auditory psychology, music theory knowledge, and pattern recognition. This paper extracts the auditory image features of chords and then establishes a sparse representation classifier model for chord recognition and classification. Scale-invariant feature transformation (SIFT) and spatial pyramid matching (SPM) are used to extract the detailed features of chord auditory images. The experimental results show that the highest correct recognition rate of the chord recognition algorithm based on the auditory image proposed in this paper is 76.2%, which is 20.4% higher than that of MFCC feature based on human auditory characteristics.

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Section: Literature Reviewmentioning

confidence: 99%

Pitch Evaluation of Matouqin Chamber Music Performance Based on Artificial Neural Network

2022

Mobile Information Systems

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“…[6][7][8][9] In particular, NNs with delays can be stabilized by introducing appropriate controllers. Cao et al 10 discussed the stabilization of MNNs by designing appropriate event-based controllers, while Wang et al 11 proved that the stabilization of MNNs by using the fuzzy membership functions-dependent on Lyapunov-Krasovskii functional. The existence and exponential stabilization of the system at the equilibrium point were discussed in other studies, [12][13][14][15][16] and the exponential synchronization and stabilization of NNs were investigated in other studies [17][18][19][20][21] by adopting the sampled-data control.…”

Section: Introductionmentioning

confidence: 99%

Exponential and fixed‐time stabilization of memristive neural networks with mixed delays

Liu

Gao

2021

Math Methods in App Sciences

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In this paper, we study a class of memristive neural networks with mixed delays. The existence of its equilibrium point is proved without the boundedness and initial value of the activation function, and a criterion to ensure its exponential stabilization under a sampled-data controller is obtained by constructing an appropriate Lyapunov function. Meanwhile, the fixed-time stabilization of memristive neural networks is also proved by the Lyapunov method. The obtained results extend and enhance some existing ones, and are illustrated by numerical examples.

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“…Recently, for stabilizing MJSs, various control strategies have been proposed, such as state feedback control, adaptive control, and sliding mode control . Among them, with the development of digital technology and communication, sampled‐data control has attracted much more attention because of its low cost consumption, reliability, and easy installation . Additionally, in practical implementation, especially under the network environment, computation and communication resources are usually limited.…”

Section: Introductionmentioning

confidence: 99%

“…20,21 Among them, with the development of digital technology and communication, sampled-data control has attracted much more attention because of its low cost consumption, reliability, and easy installation. [22][23][24][25][26][27] Additionally, in practical implementation, especially under the network environment, computation and communication resources are usually limited. Therefore, a core problem is to achieve a controller that less transmission burden can be carried.…”

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confidence: 99%

Strictly dissipative stabilization of multiple‐memory Markov jump systems with general transition rates: A novel event‐triggered control strategy

Xie

Zhang

Zeng

et al. 2020

Intl J Robust & Nonlinear

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Summary This paper investigates the strictly dissipative stabilization problem for multiple‐memory Markov jump systems with network communication protocol. Firstly, for reducing data transmission, we put forward a novel mode‐dependent event‐triggered communication scheme based on aperiodically sampled data. Secondly, a Markov jump system with general transition rates is considered to make the result more applicable, where the transition rates of some jumping modes allow to be completely known, or partially known, or even completely unknown. Thirdly, a less restrictive Lyapunov‐Krasovskii functional, which is only required to be positive definite at end points of each subinterval of the holding intervals, is first introduced for event‐triggered control issue. Based on the above methods, a sufficient condition with less conservatism is obtained to ensure the stochastic stability and dissipativity of the resulting closed‐loop system. Meanwhile, an explicit design method of the desired controller is achieved. Finally, two numerical examples are presented to demonstrate the effectiveness and advantage of the proposed method.

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A Switched Operation Approach to Sampled-Data Control Stabilization of Fuzzy Memristive Neural Networks With Time-Varying Delay

Cited by 67 publications

References 51 publications

Pitch Evaluation of Matouqin Chamber Music Performance Based on Artificial Neural Network

Pitch Evaluation of Matouqin Chamber Music Performance Based on Artificial Neural Network

Exponential and fixed‐time stabilization of memristive neural networks with mixed delays

Strictly dissipative stabilization of multiple‐memory Markov jump systems with general transition rates: A novel event‐triggered control strategy

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