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Echo state network — Wikipedia (en)

Source: Wikipedia (en)
Original: https://en.wikipedia.org/wiki/Echo_state_network
License (as archived): CC-BY-SA-4.0
Archived: 2026-10-07T16:52:26.600Z
SHA-256 of the archived text: f1530f9f8a077d048caed2d1a78313ced6a8eb898939126e363d325f7a80011f
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How to cite

Echo state network — Wikipedia (en) — Wikipedia (en), https://en.wikipedia.org/wiki/Echo_state_network (CC-BY-SA-4.0); archived copy: https://metraipolis.com/documento/dc_91069b286fbd (Metraipolis archive dc_91069b286fbd, sha256 f1530f9f8a077d048caed2d1a78313ced6a8eb898939126e363d325f7a80011f)

BibTeX

@misc{dc91069b286fbd,
  title = {Echo state network — Wikipedia (en)},
  howpublished = {Wikipedia (en)},
  url = {https://en.wikipedia.org/wiki/Echo_state_network},
  note = {License CC-BY-SA-4.0; archived copy https://metraipolis.com/documento/dc_91069b286fbd; sha256 f1530f9f8a077d048caed2d1a78313ced6a8eb898939126e363d325f7a80011f},
  year = {2026}
}

Archived text

Reproduced under CC-BY-SA-4.0. Attribution: Wikipedia (en), https://en.wikipedia.org/wiki/Echo_state_network. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

An echo state network (ESN) is a type of reservoir computer that uses a recurrent neural network with a sparsely connected hidden layer (with typically 1% connectivity). The connectivity and weights of hidden neurons are fixed and randomly assigned. The weights of output neurons can be learned so that the network can produce or reproduce specific temporal patterns. The main interest of this network is that although its behavior is non-linear, the only weights that are modified during training are for the synapses that connect the hidden neurons to output neurons. Thus, the error function is quadratic with respect to the parameter vector and can be differentiated easily to a linear system. Alternatively, one may consider a nonparametric Bayesian formulation of the output layer, under which: (i) a prior distribution is imposed over the output weights; and (ii) the output weights are marginalized out in the context of prediction generation, given the training data. This idea has been demonstrated in by using Gaussian priors, whereby a Gaussian process model with ESN-driven kernel function is obtained. Such a solution was shown to outperform ESNs with trainable (finite) sets of weights in several benchmarks. Some publicly available efficient implementations of ESNs are aureservoir (a C++ library for various kinds with python/numpy bindings), MATLAB, ReservoirComputing.jl (a Julia-based implementation of various types) and pyESN (for simple ESNs in Python).

Tema degli esploratori: statistica e scienza dei dati (deposito sistematico; ricerca «artificial neural network»).
Fonte: Wikipedia in inglese, voce «Echo state network» — https://en.wikipedia.org/wiki/Echo_state_network
Wikidata Q5332763 (dati Wikidata in CC0 1.0) — https://www.wikidata.org/wiki/Q5332763
Autori: i contributori di Wikipedia (cronologia: https://en.wikipedia.org/wiki/Echo_state_network?action=history).
Licenza: Creative Commons Attribuzione-Condividi allo stesso modo 4.0 (CC BY-SA 4.0) — https://creativecommons.org/licenses/by-sa/4.0/. Il riuso richiede attribuzione e stessa licenza.
Estratto dell'introduzione non modificato nel contenuto; revisione 1322440405, consultata il 2026-10-07T16:20:39.003Z tramite l'API ufficiale di Wikipedia. Impronta SHA-256 dell'estratto: cd8b3d1971c682ed5d66fa3bc960aa589e2753c41bbea93616fad13c351d5e88