Multilayered Echo State Machine: A Novel Architecture and Algorithm

Citation

Malik Z, Hussain A & Wu QJ (2017) Multilayered Echo State Machine: A Novel Architecture and Algorithm. IEEE Transactions on Cybernetics, 47 (4), pp. 946-959. https://doi.org/10.1109/TCYB.2016.2533545

Abstract
In this paper, we present a novel architecture and learning algorithm for a multilayered echo state machine (ML-ESM). Traditional echo state networks (ESNs) refer to a particular type of reservoir computing (RC) architecture. They constitute an effective approach to recurrent neural network (RNN) training, with the (RNN-based) reservoir generated randomly, and only the readout trained using a simple computationally efficient algorithm. ESNs have greatly facilitated the real-time application of RNN, and have been shown to outperform classical approaches in a number of benchmark tasks. In this paper, we introduce a novel criteria for integrating multiple layers of reservoirs within the ML-ESM. The addition of multiple layers of reservoirs are shown to provide a more robust alternative to conventional RC networks. We demonstrate the comparative merits of this approach in a number of applications, considering both benchmark datasets and real world applications.

Keywords
Learning; multiple layer network and time series neural network; neural network; Biological neural networks; Cybernetics; Neurons; Recurrent neural networks; Reservoirs; Standards; Training

Journal
IEEE Transactions on Cybernetics: Volume 47, Issue 4

• IEEE_Trans_Cybernetics_revised(accepted)-2016.pdf