The Experts below are selected from a list of 21030 Experts worldwide ranked by ideXlab platform
Daqing Jiang - One of the best experts on this subject based on the ideXlab platform.
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stationary distribution and periodic solution of stochastic chemostat models with single species growth on two nutrients
International Journal of Biomathematics, 2019Co-Authors: Miaomiao Gao, Daqing Jiang, Tasawar HayatAbstract:In this paper, we consider two chemostat models with Random Perturbation, in which single species depends on two perfectly substitutable resources for growth. For the autonomous system, we first pr...
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periodic solution for a non autonomous lotka volterra predator prey model with Random Perturbation
Journal of Mathematical Analysis and Applications, 2015Co-Authors: Daqing Jiang, Donal OreganAbstract:Abstract This paper studies a stochastic non-autonomous Lotka–Volterra predator–prey model. We prove that there exists at least one positive periodic solution under some simple and reasonable conditions. In addition we obtain sufficient conditions for persistence in mean and extinction for the stochastic non-autonomous system. Our method and results are new and the ideas could be used to study other types of non-autonomous stochastic predator–prey systems.
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analysis of autonomous lotka volterra competition systems with Random Perturbation
Journal of Mathematical Analysis and Applications, 2012Co-Authors: Daqing Jiang, Donal OʼreganAbstract:Abstract This paper discusses a Randomized n-species Lotka–Volterra competition system. We show that this system is stable in time average under certain conditions. Furthermore, there is a stationary distribution of this system, if extra conditions are satisfied. Also we give the extinction condition of this system. Finally, numerical simulations are carried out to support our results.
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global stability of two group sir model with Random Perturbation
Journal of Mathematical Analysis and Applications, 2009Co-Authors: Daqing Jiang, Ningzhong ShiAbstract:Abstract In this paper, we discuss the two-group SIR model introduced by Guo, Li and Shuai [H.B. Guo, M.Y. Li, Z. Shuai, Global stability of the endemic equilibrium of multigroup SIR epidemic models, Can. Appl. Math. Q. 14 (2006) 259–284], allowing Random fluctuation around the endemic equilibrium. We prove the endemic equilibrium of the model with Random Perturbation is stochastic asymptotically stable in the large. In addition, the stability condition is obtained by the construction of Lyapunov function. Finally, numerical simulations are presented to illustrate our mathematical findings.
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global stability and stochastic permanence of a non autonomous logistic equation with Random Perturbation
Journal of Mathematical Analysis and Applications, 2008Co-Authors: Daqing Jiang, Ningzhong ShiAbstract:Abstract This paper discusses a Randomized non-autonomous logistic equation d N ( t ) = N ( t ) [ ( a ( t ) − b ( t ) N ( t ) ) d t + α ( t ) d B ( t ) ] , where B ( t ) is a 1-dimensional standard Brownian motion. In [D.Q. Jiang, N.Z. Shi, A note on non-autonomous logistic equation with Random Perturbation, J. Math. Anal. Appl. 303 (2005) 164–172], the authors show that E [ 1 / N ( t ) ] has a unique positive T -periodic solution E [ 1 / N p ( t ) ] provided a ( t ) , b ( t ) and α ( t ) are continuous T -periodic functions, a ( t ) > 0 , b ( t ) > 0 and ∫ 0 T [ a ( s ) − α 2 ( s ) ] d s > 0 . We show that this equation is stochastically permanent and the solution N p ( t ) is globally attractive provided a ( t ) , b ( t ) and α ( t ) are continuous T -periodic functions, a ( t ) > 0 , b ( t ) > 0 and min t ∈ [ 0 , T ] a ( t ) > max t ∈ [ 0 , T ] α 2 ( t ) . By the way, the similar results of a generalized non-autonomous logistic equation with Random Perturbation are yielded.
J Souza E De Cursi - One of the best experts on this subject based on the ideXlab platform.
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parameter estimation in a trip distribution model by Random Perturbation of a descent method
Transportation Research Part B-methodological, 2001Co-Authors: Mirian Buss Goncalves, J Souza E De CursiAbstract:We consider the problem of the estimation of some parameters involved in a trip distribution model issued from the Transportation Planning. The estimators of the maximum likelihood of the model are the global minima of a non-convex functional. The numerical method must prevent convergence to local minima and we apply a new algorithm of global optimization involving Random Perturbations of the gradient method. Numerical experiments involving real data show that the method is effective to calculate.
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global optimization by Random Perturbation of the gradient method with a fixed parameter
Journal of Global Optimization, 1994Co-Authors: M Pogu, J Souza E De CursiAbstract:The paper deals with the global minimization of a differentiable cost function mapping a ball of a finite dimensional Euclidean space into an interval of real numbers. It is established that a suitable Random Perturbation of the gradient method with a fixed parameter generates a bounded minimizing sequence and leads to a global minimum: the Perturbation avoids convergence to local minima. The stated results suggest an algorithm for the numerical approximation of global minima: experiments are performed for the problem of fitting a sum of exponentials to discrete data and to a nonlinear system involving about 5000 variables. The effect of the Random Perturbation is examined by comparison with the purely deterministic gradient method.
Donal Oregan - One of the best experts on this subject based on the ideXlab platform.
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periodic solution for a non autonomous lotka volterra predator prey model with Random Perturbation
Journal of Mathematical Analysis and Applications, 2015Co-Authors: Daqing Jiang, Donal OreganAbstract:Abstract This paper studies a stochastic non-autonomous Lotka–Volterra predator–prey model. We prove that there exists at least one positive periodic solution under some simple and reasonable conditions. In addition we obtain sufficient conditions for persistence in mean and extinction for the stochastic non-autonomous system. Our method and results are new and the ideas could be used to study other types of non-autonomous stochastic predator–prey systems.
Ke Wang - One of the best experts on this subject based on the ideXlab platform.
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persistence extinction and global asymptotical stability of a non autonomous predator prey model with Random Perturbation
Applied Mathematical Modelling, 2012Co-Authors: Meng Liu, Ke WangAbstract:Abstract A two-species stochastic non-autonomous predator–prey model is investigated. Sufficient criteria for extinction, non-persistence in the mean and weak persistence in the mean are established. The critical value between persistence and extinction is obtained for each species in many cases. It is also shown that the system is globally asymptotically stable under some simple conditions. Some numerical simulations are introduced to illustrate the main results.
Bane Vasic - One of the best experts on this subject based on the ideXlab platform.
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efficient hardware implementation of probabilistic gradient descent bit flipping
IEEE Transactions on Circuits and Systems I-regular Papers, 2017Co-Authors: Fakhreddine Ghaffari, David Declercq, Bane VasicAbstract:This paper deals with the hardware implementation of the recently introduced Probabilistic Gradient-Descent Bit-Flipping (PGDBF) decoder. The PGDBF is a new type of hard-decision decoder for Low-Density Parity-Check (LDPC) code, with improved error correction performance thanks to the introduction of deliberate Random Perturbation in the computing units. In the PGDBF, the Random Perturbation operates during the bit-flipping step, with the objective to avoid the attraction of so-called trapping-sets of the LDPC code. In this paper, we propose an efficient hardware architecture which minimizes the resource overhead needed to implement the Random Perturbations of the PGDBF. Our architecture is based on the use of a Short Random Sequence (SRS) that is duplicated to fully apply the PGDBF decoding rules, and on an optimization of the maximum finder unit. The generation of good SRS is crucial to maintain the outstanding decoding performance of PGDBF, and we propose two different methods with equivalent hardware overheads, but with different behaviors on different LDPC codes. Our designs show that the improved PGDBF performance gains can be obtained with a very small additional complexity, therefore providing a competitive hard-decision LDPC decoding solution for current standards.
