Dynamic self-adaptive learning algorithm research based on T-S RBF fuzzy neutral network

Pu, Cuiping; Ren, Jie; Xue, Bin; An, Yueqi

doi:10.3233/jcm-180883

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…In addition, nonlinear neutral differential equations have numerous applications in engineering and natural sciences [6]. By using the T-S method, Pu et al [7] studied BP neural network and RBF neural network. Bharathi et al [8] investigated numerical solutions for sophistication single neutrality differential equations with time delay.…”

Section: Introductionmentioning

confidence: 99%

Delay-Dependent H∞ Control for T-S Fuzzy Systems with Local Nonlinear Models: An LMI Approach

Xia

2020

Mathematical Problems in Engineering

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This paper studies the stabilization design scheme with H∞ performance for a large class of nonlinear discrete-time systems. The system under study is modeled by Takagi-Sugeno (T-S) model with local nonlinearity and state delay. First, the model is changed into an equivalent fuzzy switching model. And then, according to projection theorem and piecewise Lyapunov function (PLF), two new H∞ control methods are proposed for fuzzy switched systems, which consider the time delay information of the system. Finally, the relationship among all fuzzy subsystems is considered. Because the results are only expressed by a series of linear matrix inequalities (LMIs), the controller can be directly designed by the linear matrix inequalities toolbox of MATLAB.

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Section: Introductionmentioning

confidence: 99%

Delay-Dependent H∞ Control for T-S Fuzzy Systems with Local Nonlinear Models: An LMI Approach

Xia

2020

Mathematical Problems in Engineering

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“…Samples are trained for learning [25,26], and then the classifier is used to identify candidate targets to obtain target location information [27], which breaks the conventional improvement algorithm that builds accurate and complex target models to improve tracking accuracy. Instead of focusing on the construction of stable and efficient classifiers to improve the performance of target recognition and tracking algorithms [28,29], which also inject new ideas into the research direction of target recognition and tracking, this method requires, with a large number of samples as support, obtaining an efficient classifier, and a very large amount of data is required [30,31]. No matter how the target tracking algorithm is improved, real time, accuracy, and stability are always the three important indicators to measure the effect of moving target tracking [32,33].…”

Section: Introductionmentioning

confidence: 99%

Using Adaptive Object Model to Basketball Tracking Algorithm and Simulation

et al. 2020

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The adaptive object model method is an effective way to develop dynamic and configurable adaptive software. It has the characteristics of metamodel, description drive, and runtime reflection. First, the core idea of the adaptive object model is explained; then, the five modes of establishing the metamodel in the adaptive object model architecture, the model engine, and supporting tools are analyzed; and the basketball tracking algorithm of the adaptive object model is discussed. Secondly, a two-dimensional joint information strategy is proposed to improve the tracking effect. When the basketball is in a very complex environment, there will always be some color information in the background that is the same as the target, which affects the effect of basketball tracking. Therefore, this paper proposes a Camshift tracking method based on the significance of histograms, through real time. The basketball movement is compared with the background histogram to continuously adjust the basketball movement tracking model. These two methods can better establish the tracking model of the basketball adaptive object, reduce the interference of background information, and achieve the effect of stable tracking of the target. The simulation experiment results show that the method proposed in this paper can effectively improve the accuracy of the basketball goal model and achieve stable tracking of the goal.

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A high performance control algorithm and implement method based on FNN for tea-fixation systems

Xu¹,

Kong

Zhang

2020

JCM

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Dynamic self-adaptive learning algorithm research based on T-S RBF fuzzy neutral network

Cited by 5 publications

References 4 publications

Delay-Dependent H∞ Control for T-S Fuzzy Systems with Local Nonlinear Models: An LMI Approach

Delay-Dependent H∞ Control for T-S Fuzzy Systems with Local Nonlinear Models: An LMI Approach

Using Adaptive Object Model to Basketball Tracking Algorithm and Simulation

A high performance control algorithm and implement method based on FNN for tea-fixation systems

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