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Exponential Stability of Generalized CohenGrossberg Neural Networks with TimeVarying Delays and ReactionDiffusion
, 2006
"... In this paper, a generalized model of CohenGrossberg neural networks (CGNNs) with timevarying delays and reactiondiffusion term is investigated. By constructing suitable Lyapunov functional, inequality technique and Mmatrix theory, some sufficient conditions for global exponential stability of g ..."
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In this paper, a generalized model of CohenGrossberg neural networks (CGNNs) with timevarying delays and reactiondiffusion term is investigated. By constructing suitable Lyapunov functional, inequality technique and Mmatrix theory, some sufficient conditions for global exponential stability
Almost Sure Asymptotic Stability for RegimeSwitching Diffusions
"... In this paper, we discuss longtime behavior of sample paths for a wide range of regimeswitching diffusions. Firstly, almost sure asymptotic stability is concerned (i) for regimeswitching diffusions with finite state spaces by the PerronFrobenius theorem, and, with regard to the case of reversibl ..."
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of reversible Markov chain, via the principal eigenvalue approach; (ii) for regimeswitching diffusions with countable state spaces by means of a finite partition trick and an MMatrix theory. We then apply our theory to study the stabilization for linear switching models. Several examples are given
Existence and Global Exponential Stability of Periodic Solution for BAM Neural Networks with Mixed TimeVarying Delays1
"... In this paper, the problem on the existence and stability of periodic solutions is investigated for bidirectional associative memory neural networks with periodic coefficients and timevarying delays and distributed delays. Under the generalization of dropping the boundedness and monotonicity of ..."
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of the activation functions as well as the differentiability of the timevarying delays, several sufficient conditions ensuring the existence, uniqueness and global exponential stability of periodic solution for the networks are derived by using analytic methods, inequality technique and Mmatrix theory. Moreover
Research Article Existence and Exponential Stability of Periodic Solution for a Class of Generalized Neural Networks with Arbitrary Delays
, 2009
"... By the continuation theorem of coincidence degree and Mmatrix theory, we obtain some sufficient conditions for the existence and exponential stability of periodic solutions for a class of generalized neural networks with arbitrary delays, which are milder and less restrictive than those of previous ..."
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By the continuation theorem of coincidence degree and Mmatrix theory, we obtain some sufficient conditions for the existence and exponential stability of periodic solutions for a class of generalized neural networks with arbitrary delays, which are milder and less restrictive than those
MONOTONE CONVERGENCE OF THE LANCZOS APPROXIMATIONS TO MATRIX FUNCTIONS OF HERMITIAN MATRICES
, 2008
"... When A is a Hermitian matrix, the action f(A)b of a matrix function f(A) on a vector b can efficiently be approximated via the Lanczos method. In this note we use Mmatrix theory to establish that the 2norm of the error of the sequence of approximations is monotonically decreasing if f is a Stiel ..."
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Cited by 3 (0 self)
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When A is a Hermitian matrix, the action f(A)b of a matrix function f(A) on a vector b can efficiently be approximated via the Lanczos method. In this note we use Mmatrix theory to establish that the 2norm of the error of the sequence of approximations is monotonically decreasing if f is a
Numerische Mathematik manuscript No. (will be inserted by the editor) Accurate Solutions of MMatrix Sylvester Equations
"... Abstract This paper is concerned with a relative perturbation theory and its entrywise relatively accurate numerical solutions of an Mmatrix Sylvester equation AX + XB = C by which we mean both A and B have positive diagonal entries and nonpositive offdiagonal entries and P = Im⊗A+BT ⊗ In is a no ..."
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Abstract This paper is concerned with a relative perturbation theory and its entrywise relatively accurate numerical solutions of an Mmatrix Sylvester equation AX + XB = C by which we mean both A and B have positive diagonal entries and nonpositive offdiagonal entries and P = Im⊗A+BT ⊗ In is a
The development and comparison of robust methods for estimating the fundamental matrix
 International Journal of Computer Vision
, 1997
"... Abstract. This paper has two goals. The first is to develop a variety of robust methods for the computation of the Fundamental Matrix, the calibrationfree representation of camera motion. The methods are drawn from the principal categories of robust estimators, viz. case deletion diagnostics, Mest ..."
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Cited by 266 (10 self)
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Abstract. This paper has two goals. The first is to develop a variety of robust methods for the computation of the Fundamental Matrix, the calibrationfree representation of camera motion. The methods are drawn from the principal categories of robust estimators, viz. case deletion diagnostics, M
Development of a Twodimensional Finite Element Model for Pure Advective Equation
, 2003
"... This article deals with a sixparameter flux corrected transport (FCT) Taylor Galerkin finite element model for solving the pure convection equation that admits discontinuities. Modified equation analysis is conducted to optimize the scheme accuracy in the smooth flow. In the presence of discontinui ..."
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oscillations. The success in employing the FCT technique of Zalesak is to obtain a monotone solution and we apply the Mmatrix theory to achieve the goal. To validate the proposed finite element model, analytic tests, which are amenable to smooth as well as sharply varying solutions, are conducted. © 2003
Research Article Exponential Stability for Impulsive BAM Neural Networks with TimeVarying Delays and ReactionDiffusion Terms
, 2007
"... Impulsive bidirectional associative memory neural network model with timevarying delays and reactiondiffusion terms is considered. Several sufficient conditions ensuring the existence, uniqueness, and global exponential stability of equilibrium point for the addressed neural network are derived ..."
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by Mmatrix theory, analytic methods, and inequality techniques. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. The obtained results in this paper are less restrictive than previously known criteria. Two examples are given to show
Stability Analysis of Impulsive Fuzzy Recurrent Neural Networks with Hybrid Delays1
"... Abstract. In this paper, the impulsive fuzzy recurrent neural network with both timevarying delays and distributed delays is considered. Applying the idea of vector Lyapunov function, Mmatrix theory and analytic methods, several sufficient conditions are obtained to ensure the existence, uniquenes ..."
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Abstract. In this paper, the impulsive fuzzy recurrent neural network with both timevarying delays and distributed delays is considered. Applying the idea of vector Lyapunov function, Mmatrix theory and analytic methods, several sufficient conditions are obtained to ensure the existence
Results 11  20
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