Analysis and Design of Echo State Networks

DOI10.1162/neco.2007.19.1.111zbMath1125.68102OpenAlexW2029967456WikidataQ51099657 ScholiaQ51099657MaRDI QIDQ5758069

Dongming Xu, Mustafa C. Ozturk, Jose C. Principe

Publication date: 31 August 2007

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/neco.2007.19.1.111

zbMATH Keywords

ESN dynamicsinformation-theoretic metric

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Modes of computation (nondeterministic, parallel, interactive, probabilistic, etc.) (68Q10)

Related Items (15)

Optimization and applications of echo state networks with leaky- integrator neurons ⋮ An associative memory readout for ESNs with applications to dynamical pattern recognition ⋮ Unnamed Item ⋮ Online sequential echo state network with sparse RLS algorithm for time series prediction ⋮ Echo state network with a global reversible autoencoder for time series classification ⋮ Re-visiting the echo state property ⋮ Identification of dual‐rate sampled nonlinear systems based on the cycle reservoir with regular jumps network ⋮ Wavelet-denoising multiple echo state networks for multivariate time series prediction ⋮ Reservoir computing approaches to recurrent neural network training ⋮ Numerical solution and bifurcation analysis of nonlinear partial differential equations with extreme learning machines ⋮ A constrained regularization approach for input-driven recurrent neural networks ⋮ Memory in linear recurrent neural networks in continuous time ⋮ Heuristic Dynamic Programming Using Echo State Network For Multivariable Tracking Control Of Wastewater Treatment Process ⋮ FREEMAN'S K MODELS AS RESERVOIR COMPUTING ARCHITECTURES ⋮ Design Strategies for Weight Matrices of Echo State Networks

Cites Work

• Turing computability with neural nets
• Nonlinearity creates linear independence
• The gamma-filter-a new class of adaptive IIR filters with restricted feedback
• Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations
• Real-Time Computation at the Edge of Chaos in Recurrent Neural Networks
• Dynamic recurrent neural network for system identification and control
• Neurons with graded response have collective computational properties like those of two-state neurons.
• Probability, Frequency and Reasonable Expectation
• An invariant form for the prior probability in estimation problems

This page was built for publication: Analysis and Design of Echo State Networks