Search results for "Discrete-time systems"

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Finite-time boundedness for uncertain discrete neural networks with time-delays and Markovian jumps

2014

This paper is concerned with stochastic finite-time boundedness analysis for a class of uncertain discrete-time neural networks with Markovian jump parameters and time-delays. The concepts of stochastic finite-time stability and stochastic finite-time boundedness are first given for neural networks. Then, applying the Lyapunov approach and the linear matrix inequality technique, sufficient criteria on stochastic finite-time boundedness are provided for the class of nominal or uncertain discrete-time neural networks with Markovian jump parameters and time-delays. It is shown that the derived conditions are characterized in terms of the solution to these linear matrix inequalities. Finally, n…

Lyapunov functionDiscrete-time systems; Linear matrix inequalities; Markovian jump systems; Neural networks; Stochastic finite-time boundedness; Artificial Intelligence; Computer Science Applications1707 Computer Vision and Pattern Recognition; Cognitive NeuroscienceArtificial neural networkMarkov chainStochastic processCognitive NeuroscienceMarkovian jump systemsLinear matrix inequalitiesLinear matrix inequalityComputer Science Applications1707 Computer Vision and Pattern RecognitionComputer Science Applicationssymbols.namesakeDiscrete time and continuous timeArtificial IntelligenceDiscrete-time systemssymbolsCalculusApplied mathematicsStochastic neural networkJump processNeural networksStochastic finite-time boundednessMathematics
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On integral input-to-state stability for a feedback interconnection of parameterised discrete-time systems

2014

This paper addresses integral input-to-state stability iISS for a feedback interconnection of parameterised discrete-time systems involving two subsystems. Particularly, we give a construction for a smooth iISS Lyapunov function for the whole system from the sum of nonlinearly weighted Lyapunov functions of individual subsystems. Motivations for such a construction are given. We consider two main cases. The first one investigates iISS for the whole system when both subsystems are iISS. The second one gives iISS for the interconnected system when one of subsystems is allowed to be input-to-state stable. The approach is also valid for both discrete-time cascades and a feedback interconnection…

Lyapunov functionsmall-gain conditions0209 industrial biotechnologyInterconnectionStability (learning theory)Computer Science Applications1707 Computer Vision and Pattern Recognition02 engineering and technologyState (functional analysis)Computer Science ApplicationsWhole systems0-global asymptotic stabilityTheoretical Computer Scienceinput-to-state stabilitysymbols.namesakeparameterised discrete-time systems020901 industrial engineering & automationDiscrete time and continuous timeControl theoryControl and Systems Engineering0202 electrical engineering electronic engineering information engineeringsymbols020201 artificial intelligence & image processing0-global asymptotic stability; input-to-state stability; integral input-to-state stability; parameterised discrete-time systems; small-gain conditions; Control and Systems Engineering; Theoretical Computer Science; Computer Science Applications1707 Computer Vision and Pattern Recognitionintegral input-to-state stabilityMathematics
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