Nothing Special   »   [go: up one dir, main page]

Yang et al., 2018 - Google Patents

Finite-time boundedness and stabilization of uncertain switched delayed neural networks of neutral type

Yang et al., 2018

Document ID
3634452537856470320
Author
Yang X
Tian Y
Li X
Publication year
Publication venue
Neurocomputing

External Links

Snippet

In this paper, we investigate the finite-time boundedness (FTB) and finite-time stabilization (FTS) of uncertain switched delayed neural networks of neutral type. Some sufficient conditions are derived in terms of linear matrix inequalities (LMIs) to guarantee the FTB and …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/06Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G06N3/0635Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means using analogue means
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/04Architectures, e.g. interconnection topology
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations
    • G06F17/11Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • G06F17/13Differential equations
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B17/00Systems involving the use of models or simulators of said systems
    • G05B17/02Systems involving the use of models or simulators of said systems electric
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B11/00Automatic controllers
    • G05B11/01Automatic controllers electric
    • G05B11/32Automatic controllers electric with inputs from more than one sensing element; with outputs to more than one correcting element

Similar Documents

Publication Publication Date Title
Xia et al. Non-fragile finite-time extended dissipative control for a class of uncertain discrete time switched linear systems
Yang et al. Finite-time boundedness and stabilization of uncertain switched delayed neural networks of neutral type
Anbuvithya et al. Non-fragile synchronization of memristive BAM networks with random feedback gain fluctuations
Ali et al. New passivity criteria for memristor-based neutral-type stochastic BAM neural networks with mixed time-varying delays
Wen et al. Circuit design and exponential stabilization of memristive neural networks
Kwon et al. New approaches on stability criteria for neural networks with interval time-varying delays
Shen et al. Extended passive filtering for discrete-time singular Markov jump systems with time-varying delays
Zouari et al. Neural adaptive quantized output-feedback control-based synchronization of uncertain time-delay incommensurate fractional-order chaotic systems with input nonlinearities
Liu et al. Finite-time synchronization of neutral complex networks with Markovian switching based on pinning controller
Wang et al. Adaptive neural control for high order Markovian jump nonlinear systems with unmodeled dynamics and dead zone inputs
Sakthivel et al. Finite-time fault-tolerant control of neutral systems against actuator saturation and nonlinear actuator faults
Wu et al. New stability and stabilization conditions for stochastic neural networks of neutral type with Markovian jumping parameters
Ali et al. Robust finite-time H∞ control for a class of uncertain switched neural networks of neutral-type with distributed time varying delays
Sakthivel et al. Dissipativity based repetitive control for switched stochastic dynamical systems
Rakkiyappan et al. Effects of leakage time-varying delays in Markovian jump neural networks with impulse control
Shi et al. Finite-time output feedback control for discrete-time switched linear systems with mode-dependent persistent dwell-time
Arunkumar et al. Robust stability criteria for discrete-time switched neural networks with various activation functions
Popa Mittag–Leffler stability and synchronization of neutral-type fractional-order neural networks with leakage delay and mixed delays
Balasundaram et al. New global asymptotic stability of discrete-time recurrent neural networks with multiple time-varying delays in the leakage term and impulsive effects
Li et al. H∞ control of Markov jump systems with time-varying delay and incomplete transition probabilities
Cui et al. Finite-time synchronization of inertial neural networks
Younsi et al. Decentralized Control Design for Switching Fuzzy Large-Scale T–S Systems by Switched Lyapunov Function with H_ ∞ H∞ Performance
Wei et al. New results on passivity analysis of memristive neural networks with time-varying delays and reaction–diffusion term
Miaadi et al. Impulsive effect on fixed-time control for distributed delay uncertain static neural networks with leakage delay
Gholami et al. Finite-time H∞ static and dynamic output feedback control for a class of switched nonlinear time-delay systems