6533b856fe1ef96bd12b1c22

RESEARCH PRODUCT

Neural Network Based Finite-Time Stabilization for Discrete-Time Markov Jump Nonlinear Systems with Time Delays

Hamid Reza KarimiFei LiuFei Chen

subject

Time delaysArticle SubjectState-space representationArtificial neural networklcsh:MathematicsApplied MathematicsParameterized complexitylcsh:QA1-939VDP::Mathematics and natural science: 400::Mathematics: 410::Analysis: 411Nonlinear systemDiscrete time and continuous timeControl theoryJumpAnalysisMathematicsMarkov jump

description

Published version of an article in the journal: Abstract and Applied Analysis. Also available from the publisher at: http://dx.doi.org/10.1155/2013/359265 Open Access This paper deals with the finite-time stabilization problem for discrete-time Markov jump nonlinear systems with time delays and norm-bounded exogenous disturbance. The nonlinearities in different jump modes are parameterized by neural networks. Subsequently, a linear difference inclusion state space representation for a class of neural networks is established. Based on this, sufficient conditions are derived in terms of linear matrix inequalities to guarantee stochastic finite-time boundedness and stochastic finite-time stabilization of the closed-loop system. A numerical example is illustrated to verify the efficiency of the proposed technique.

yearjournalcountryeditionlanguage
2013-01-01Abstract and Applied Analysis
https://doi.org/10.1155/2013/359265