Stable adaptive control with recurrent networks

Kulawski, G.J.; Brdy, Mietek A.

doi:10.1016/s0005-1098(99)00092-8

Cited by 36 publications

(9 citation statements)

References 24 publications

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“…G. J. Kulawski et al (2000) [15] first gave the setting solution of adaptive law for uncertain nonlinear system through the dynamic neural network. We use adaptive law, external law and lumped law to represent the feedbacks, and do assignment separately according to the simulation of real data.…”

Section: Fuzzy Control Unitmentioning

confidence: 99%

Research on Diffluent Network Prediction Model of Ecotourim Scenic Spot Based on the Dynamic Fuzzy Control

Peng¹,

Ren²,

Jin³

et al. 2013

Appl. Math. Inf. Sci.

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Abstract:The acute contradiction between tourism economic development and ecological environment protection is becoming increasingly serious in many natural scenic spots. It became the focus of both managers of scenic area and scholars that using advanced information technology to track the swath law of tourists and realizing real-time control of the time and space distribution of visitors in scenic area. This paper adopted the idea of fuzzy control, constructed a dynamic fuzzy feedback control forecast model based on the adaptive laws at first, then it proved two kinds of effects existed in this model. Finally, it simulated a single units attraction, and the result of comparative analysis with current state of experience based schedule proved the effectiveness of the system proposed in this paper. This formed the foundation for the real-time scheduling in future study.

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Section: Fuzzy Control Unitmentioning

confidence: 99%

Research on Diffluent Network Prediction Model of Ecotourim Scenic Spot Based on the Dynamic Fuzzy Control

Peng¹,

Ren²,

Jin³

et al. 2013

Appl. Math. Inf. Sci.

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“…On the basis of the formulation of stochastic recurrent neural networks [17], we consider the following stochastic recurrent neural network, which is similar to the model of deterministic recurrent neural networks defined in [9,18] plus an additive white noise:…”

Section: Problem Formulation and Mathematical Preliminariesmentioning

confidence: 99%

“…Especially, in the area of system identification and control, there have been considerable interests in exploring the applications of recurrent neural networks to deal with uncertainties and unknown external disturbances for nonlinear system control. The study of both stability and controllability of recurrent neural networks have received much attention in the last few years [1][2][3][4][5][6][7][8][9]. However, the neural networks discussed in the above publications are deterministic recurrent neural networks.…”

Section: Introductionmentioning

confidence: 99%

Inverse optimal noise‐to‐state stabilization of stochastic recurrent neural networks driven by noise of unknown covariance

Liu

Wang

Schurz

2008

Optim Control Appl Methods

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In this paper, we extend our previous research results regarding the stabilization of recurrent neural networks from the concept of input-to-state stability to noise-to-state stability, and present a new approach to achieve noise-to-state stabilization in probability for stochastic recurrent neural networks driven by the noise of unknown covariance. This approach is developed by using the Lyapunov technique, inverse optimality, differential game theory, and the Hamilton-Jacobi-Isaacs equation. Numerical examples demonstrate the effectiveness of the proposed approach.

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“…Some are based neural networks [1], [2], [3], [4], some on differential geometry [5], [6], some on fuzzy logic [4], [7], etc.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy predictive functional control with adaptive fuzzy model

Dovžan

Škrjanc

2010

2010 International Joint Conference on Computational Cybernetics and Technical Informatics

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Stable adaptive control with recurrent networks

Cited by 36 publications

References 24 publications

Research on Diffluent Network Prediction Model of Ecotourim Scenic Spot Based on the Dynamic Fuzzy Control

Research on Diffluent Network Prediction Model of Ecotourim Scenic Spot Based on the Dynamic Fuzzy Control

Inverse optimal noise‐to‐state stabilization of stochastic recurrent neural networks driven by noise of unknown covariance

Fuzzy predictive functional control with adaptive fuzzy model

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