Combined H∞ and passivity state estimation of memristive neural networks with random gain fluctuations

Neurocomputing

Volumn 168, Issue , 2015, Pages 1111-1120

  Sakthivel, Rathinasamy ^a,b   Anbuvithya, Ranganathan ^c   Mathiyalagan, Kalidass ^d   Prakash, Periasamy ^c  

^a SRI RAMAKRISHNA INSTITUTE OF TECHNOLOGY   (India)

^b Sungkyunkwan University   (South Korea)

^c PERIYAR UNIVERSITY   (India)

^d ANNA UNIVERSITY   (India)

Author keywords

Different memductance function; Memristor; Non fragile control; Random fluctuation; Recurrent neural network

Indexed keywords

ASYMPTOTIC STABILITY; LINEAR MATRIX INEQUALITIES; LYAPUNOV FUNCTIONS; RECURRENT NEURAL NETWORKS; STATE ESTIMATION; TIME VARYING NETWORKS;

GLOBAL ASYMPTOTIC STABILITY; LINEAR MATRIX INEQUALITY APPROACH; LYAPUNOV-KRASOVSKII FUNCTIONAL (LKF); MEMRISTOR; NON- FRAGILE CONTROLS; NON-FRAGILE OBSERVERS; PERFORMANCE CRITERION; RANDOM FLUCTUATION;

CLOSED LOOP SYSTEMS;

ARTICLE; CALCULATION; LYAPUNOV KRASOVSKII FUNCTIONAL; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; MEMRISTIVE RECURRENT NEURAL NETWORK; NERVE CELL NETWORK; PRIORITY JOURNAL; SIMULATION; STOCHASTIC MODEL; VALIDITY;

EID: 84937816725     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2015.05.012     Document Type: Article

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