Litcius/Paper detail

Echo State Networks trained by Tikhonov least squares are <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e144" altimg="si5.svg"><mml:mrow><mml:msup><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup><mml:mrow><mml:mo>(</mml:mo><mml:mi>μ</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math> approximators of ergodic dynamical systems

Allen Hart, James Hook, Jonathan Dawes

2021Physica D Nonlinear Phenomena53 citationsDOIOpen Access PDF

Topics & Concepts

Tikhonov regularizationAlgorithmMathematicsBiorthogonal systemLinear regressionArtificial intelligenceComputer scienceDiscrete mathematicsMathematical analysisInverse problemStatisticsWavelet transformWaveletNeural Networks and Reservoir ComputingModel Reduction and Neural NetworksNeural dynamics and brain function
Echo State Networks trained by Tikhonov least squares are <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e144" altimg="si5.svg"><mml:mrow><mml:msup><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup><mml:mrow><mml:mo>(</mml:mo><mml:mi>μ</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math> approximators of ergodic dynamical systems | Litcius