Saving Energy in Wireless Sensor Networks Based on Echo State Networks

Qin, Ling; Rong-qiang, HU; Zhang, Qi

doi:10.1007/978-3-642-01513-7_101

Cited by 1 publication

(1 citation statement)

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“…ESN-based models have also been used in manufacturing, such as motor control [218], detection and diagnosis of anomaly and fault [219,220,221,222,106,223,89,72], production system monitor [224,225] and communications of sensors [226,227,228,229,230,231], mobile [116,232], satellite [233], wireless [234] and 5G Systems [235].…”

Section: Real-world Tasks Orientatedmentioning

confidence: 99%

A Review of Designs and Applications of Echo State Networks

Sun¹,

Song²,

Hong³

et al. 2020

Preprint

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Recurrent Neural Networks (RNNs) have demonstrated their outstanding ability in sequence tasks and have achieved state-of-the-art in wide range of applications, such as industrial, medical, economic and linguistic. Echo State Network (ESN) is simple type of RNNs and has emerged in the last decade as an alternative to gradient descent training based RNNs. ESN, with a strong theoretical ground, is practical, conceptually simple, easy to implement. It avoids non-converging and computationally expensive in the gradient descent methods. Since ESN was put forward in 2002, abundant existing works have promoted the progress of ESN, and the recently introduced Deep ESN model opened the way to uniting the merits of deep learning and ESNs. Besides, the combinations of ESNs with other machine learning models have also overperformed baselines in some applications. However, the apparent simplicity of ESNs can sometimes be deceptive and successfully applying ESNs needs some experience. Thus, in this paper, we categorize the ESN-based methods to basic ESNs, DeepESNs and combinations, then analyze them from the perspective of theoretical studies, network designs and specific applications. Finally, we discuss the challenges and opportunities of ESNs by summarizing the open questions and proposing possible future works.

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