English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper explores the problem of training a recurrent neural network fromnoisy data. While neural network based dynamic predictors perform well withnoise-free training data, prediction with noisy inputs during training phaseposes a significant challenge. Here a sequential training algorithm isdeveloped for an echo-state network (ESN) by incorporating noisy observationsusing an ensemble Kalman filter. The resultant Kalman-trained echo-statenetwork (KalT-ESN) outperforms the traditionally trained ESN with least squarealgorithm while still being computationally cheap. The proposed method isdemonstrated on noisy observations from three systems: two synthetic datasetsfrom chaotic dynamical systems and a set of real-time traffic data.

Sequential Learning from Noisy Data: Data-Assimilation Meets Echo-State Network