Impact of time-history terms on reservoir dynamics and prediction accuracy in echo state networks.

ABSTRACT: The echo state network (ESN) is an excellent machine learning model for processing time-series data. This model, utilising the response of a recurrent neural network, called a reservoir, to input signals, achieves high training efficiency. Introducing time-history terms into the neuron model of the reservoir is known to improve the time-series prediction performance of ESN, yet the reasons for this improvement have not been quantitatively explained in terms of reservoir dynamics characteristics. Therefore, we hypothesised that the performance enhancement brought about by time-history terms could be explained by delay capacity, a recently proposed metric for assessing the memory performance of reservoirs. To test this hypothesis, we conducted comparative experiments using ESN models with ti

SUBMITTER: Ebato Y 

PROVIDER: S-EPMC11018609 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature