Litcius/Paper detail

Novel results on asymptotic stability and synchronization of fractional-order memristive neural networks with time delays: The <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg" display="inline" id="d1e545"><mml:mrow><mml:mn>0</mml:mn><mml:mo linebreak="goodbreak" linebreakstyle="after">&amp;lt;</mml:mo><mml:mi>δ</mml:mi><mml:mo linebreak="goodbreak" linebreakstyle="after">≤</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:math> case

Jia‐Rui Zhang, Jun-Guo Lu, Xiao‐Chuang Jin, Xing-Yu Yang

2023Neural Networks20 citationsDOI

Topics & Concepts

Synchronization (alternating current)Artificial neural networkLaplace transformStability (learning theory)Exponential stabilityInverseOrder (exchange)Nonlinear systemApplied mathematicsMathematicsComputer scienceAlgorithmTopology (electrical circuits)Artificial intelligenceMachine learningCombinatoricsMathematical analysisEconomicsFinancePhysicsGeometryQuantum mechanicsNeural Networks Stability and Synchronizationstochastic dynamics and bifurcationAdvanced Memory and Neural Computing
Novel results on asymptotic stability and synchronization of fractional-order memristive neural networks with time delays: The <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg" display="inline" id="d1e545"><mml:mrow><mml:mn>0</mml:mn><mml:mo linebreak="goodbreak" linebreakstyle="after">&amp;lt;</mml:mo><mml:mi>δ</mml:mi><mml:mo linebreak="goodbreak" linebreakstyle="after">≤</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:math> case | Litcius