The Experts below are selected from a list of 16263 Experts worldwide ranked by ideXlab platform
Haijun Jiang - One of the best experts on this subject based on the ideXlab platform.
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lag synchronization for cohen grossberg neural networks with mixed time delays via periodically intermittent control
International Journal of Computer Mathematics, 2017Co-Authors: Abdujelil Abdurahman, Haijun Jiang, Zhidong TengAbstract:In this paper, the exponential lag synchronization for a class of Cohen–Grossberg neural networks with discrete time-delays and distributed delays is investigated via periodically intermittent control. Some simple and useful criteria are derived by using Mathematical Induction method and the analysis technique which are different from the methods employed in correspondingly previous works. Finally, two examples and their numerical simulations are given to demonstrate the effectiveness of the proposed control schemes.
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exponential synchronization for delayed recurrent neural networks via periodically intermittent control
Neurocomputing, 2013Co-Authors: Jiuju Xing, Haijun JiangAbstract:In this paper, the exponential synchronization for a class of delayed recurrent neural networks is investigated by virtue of intermittent control schemes. Based on p-norm and ~-norm, respectively, several new and useful synchronization criteria are derived by applying Lyapunov functional theory, Mathematical Induction and inequality technique. Particularly, some feasible regions of control parameters for each neuron are derived for the realization of exponential synchronization, which provides great convenience to the applications of the theoretical results. Finally, some numerical simulations are given to demonstrate the effectiveness of the proposed control methods.
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exponential lag synchronization for neural networks with mixed delays via periodically intermittent control
Chaos, 2010Co-Authors: Haijun Jiang, Zhidong TengAbstract:In this paper, the exponential lag synchronization for a class of neural networks with discrete delays and distributed delays is studied via periodically intermittent control for the first time. Some novel and useful criteria are derived by using Mathematical Induction method and the analysis technique which are different from the methods employed in correspondingly previous works. Finally, some numerical simulations are given to demonstrate the effectiveness of the proposed control methods.
R Rakkiyappan - One of the best experts on this subject based on the ideXlab platform.
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exponential input to state stability of stochastic cohen grossberg neural networks with mixed delays
Nonlinear Dynamics, 2015Co-Authors: R RakkiyappanAbstract:In this paper, we study an issue of input-to-state stability analysis for a class of impulsive stochastic Cohen–Grossberg neural networks with mixed delays. The mixed delays consist of varying delays and continuously distributed delays. To the best of our knowledge, the input-to-state stability problem for this class of stochastic system has still not been solved, despite its practical importance. The main aim of this paper is to fill the gap. By constricting several novel Lyapunov–Krasovskii functionals and using some techniques such as the It\(\hat{o}\) formula, Dynkin formula, impulse theory, stochastic analysis theory, and the Mathematical Induction, we obtain some new sufficient conditions to ensure that the considered system with/without impulse control is mean-square exponentially input-to-state stable. Moreover, the obtained results are illustrated well with two numerical examples and their simulations.
Zhidong Teng - One of the best experts on this subject based on the ideXlab platform.
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lag synchronization for cohen grossberg neural networks with mixed time delays via periodically intermittent control
International Journal of Computer Mathematics, 2017Co-Authors: Abdujelil Abdurahman, Haijun Jiang, Zhidong TengAbstract:In this paper, the exponential lag synchronization for a class of Cohen–Grossberg neural networks with discrete time-delays and distributed delays is investigated via periodically intermittent control. Some simple and useful criteria are derived by using Mathematical Induction method and the analysis technique which are different from the methods employed in correspondingly previous works. Finally, two examples and their numerical simulations are given to demonstrate the effectiveness of the proposed control schemes.
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exponential lag synchronization for neural networks with mixed delays via periodically intermittent control
Chaos, 2010Co-Authors: Haijun Jiang, Zhidong TengAbstract:In this paper, the exponential lag synchronization for a class of neural networks with discrete delays and distributed delays is studied via periodically intermittent control for the first time. Some novel and useful criteria are derived by using Mathematical Induction method and the analysis technique which are different from the methods employed in correspondingly previous works. Finally, some numerical simulations are given to demonstrate the effectiveness of the proposed control methods.
Raymond Reiter - One of the best experts on this subject based on the ideXlab platform.
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proving properties of states in the situation calculus
Artificial Intelligence, 1993Co-Authors: Raymond ReiterAbstract:Abstract In the situation calculus, it is sometimes necessary to prove that certain properties are true in all world states accessible from the initial state. This is the case for some forms of reasoning about the physical world, for certain planning applications, and for verifying integrity constraints in databases. Not surprisingly, this requires a suitable form of Mathematical Induction. This paper motivates the need for proving properties of states in the situation calculus, proposes appropriate Induction principles for this task, and gives examples of their use in databases and for reasoning about the physical world.
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the projection problem in the situation calculus a soundness and completeness result with an application to database updates
International Conference on Artificial Intelligence Planning Systems, 1992Co-Authors: Raymond ReiterAbstract:Abstract We describe a novel application of planning in the situation calculus to formalize the evolution of a database under update transactions. In the resulting theory, query evaluation becomes identical to the temporal projection problem. We next define a class of axioms for which the classical AI planning technique of goal regression provides a sound and complete method for solving the projection problem, hence for querying evolving databases. Finally, we briefly discuss several issues which naturally arise in the settings of databases and planning, namely, proofs by Mathematical Induction of properties of world states, logic programming implementations of the projection problem, and historical queries.
Sadia Arshad - One of the best experts on this subject based on the ideXlab platform.
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trapezoidal scheme for time space fractional diffusion equation with riesz derivative
Journal of Computational Physics, 2017Co-Authors: Sadia Arshad, Jianfei Huang, Abdul Q M Khaliq, Yifa TangAbstract:Abstract In this paper, a finite difference scheme is proposed to solve time–space fractional diffusion equation which has second-order accuracy in both time and space direction. The time and space fractional derivatives are considered in the senses of Caputo and Riesz, respectively. First, the centered difference approach is used to approximate the Riesz fractional derivative in space. Then, the obtained fractional ordinary differential equations are transformed into equivalent Volterra integral equations. And then, the trapezoidal rule is utilized to approximate the Volterra integral equations. The stability and convergence of our scheme are proved via Mathematical Induction method. Finally, numerical experiments are performed to confirm the high accuracy and efficiency of our scheme.
