• Journal of applied mathematics & informatics
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• v.30 no.1_2
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• pp.83-92
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• 2012

In this paper, a generalized Shannon-McMillan theorem for the nonhomogeneous Markov chains indexed by an infinite tree which has a uniformly bounded degree is discussed by constructing a nonnegative martingale and analytical methods. As corollaries, some Shannon-Mcmillan theorems for the nonhomogeneous Markov chains indexed by a homogeneous tree and the nonhomogeneous Markov chain are obtained. Some results which have been obtained are extended.

• Bulletin of the Korean Mathematical Society
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• v.59 no.5
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• pp.1305-1315
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• 2022

Let u be a function on a connected finite graph G = (V, E). We consider the mean field equation (1) $-{\Delta}u={\rho}\({\frac{he^u}{\int_Vhe^ud{\mu}}}-{\frac{1}{{\mid}V{\mid}}}\),$ where ∆ is 𝜇-Laplacian on the graph, 𝜌 ∈ ℝ\{0}, h : V → ℝ⁺ is a function satisfying min_x∈V h(x) > 0. Following Sun and Wang [15], we use the method of Brouwer degree to prove the existence of solutions to the mean field equation (1). Firstly, we prove the compactness result and conclude that every solution to the equation (1) is uniformly bounded. Then the Brouwer degree can be well defined. Secondly, we calculate the Brouwer degree for the equation (1), say $$d_{{\rho},h}=\{{-1,\;{\rho}>0, \atop 1,\;{\rho}<0.}$$ Consequently, the equation (1) has at least one solution due to the Brouwer degree d_𝜌,h ≠ 0.

• Journal of Applied and Pure Mathematics
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• v.4 no.5_6
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• pp.269-286
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• 2022

In this article we exhibit univariate and multivariate quantitative approximation by Kantorovich-Choquet type quasi-interpolation neural network operators with respect to supremum norm. This is done with rates using the first univariate and multivariate moduli of continuity. We approximate continuous and bounded functions on ℝ^N , N ∈ ℕ. When they are also uniformly continuous we have pointwise and uniform convergences. Our activation functions are induced by the arctangent, algebraic, Gudermannian and generalized symmetrical sigmoid functions.

• Bulletin of the Korean Mathematical Society
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• v.36 no.1
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• pp.147-159
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• 1999

Tabanov investigated the global symmetric perturbation of the integrable billiard mapping in the ellipse [3]. He showed the nonintegrability of the Birkhoff billiard in the perturbed domain by proving that the principal separatrices splitting angle is not zero.In this paper, using the exact separatrix map of an one-degree-of freedom Hamiltoniam system with time periodic perturbation, we show the existence the stochastic layer including the uniformly hyperbolic invariant set which implies the nonintegrability near the separatrices of a Birkhoff's billiard in the domain bounded by $C^2$ convex simple curve constructed by the generic global perturbation of the ellipse.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.371-373
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• 1992

This note proposes a novel robust adaptive control algorithm for systems with unknown disturbances by introducing an additional term in the control input. This additional term is easily implementable by estimating the upper bound of the unknown disturbances. By this term, the output error can be made to be uniformly ultimately bounded in a desired region via Lyapunov second stability theorem when the relative degree of system is one.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.58-61
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• 1996

For a class of single-input single-output continuous-time nonlinear systems, a multilayer neural network-based controller that feedback-linearizes the system is presented. Control action is used to achieve tracking performance for a state-feedback linearizable but unknown nonlinear system. The multilayer neural network(NN) is used to approximate nonlinear continuous function to any desired degree of accuracy. The weight-update rule of multilayer neural network is derived to satisfy Lyapunov stability. It is shown that all the signals in the closed-loop system are uniformly bounded. Initialization of the network weights is straightforward.

• Journal of IKEEE
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• v.9 no.1 s.16
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• pp.72-78
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• 2005

This paper describes the design of a robust output-feedback controller for a single-input single-output nonlinear dynamical system with a full relative degree. While all the previous research works on the output-feedback control are based on dynamic observers, a new state estimator which uses the past values of the measurable system output is proposed. We name it backward-difference state estimator since the derivatives of the output are estimated simply by backward difference of the present and past values of the output. The disturbance generated due to the error between the estimated and real state variables is compensated using an additional robustifying control law whose gain is tuned adaptively. Overall control system guarantees that the tracking error is asymptotically convergent and that all signals involved are uniformly bounded. Theoretical results are illustrated through a simulation example of inverted pendulum.