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Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Multilayered Echo State Machine: A Novel Architecture and Algorithm
Author(s): Malik, Zeeshan
Hussain, Amir
Wu, Qingming Jonathan
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Keywords: Learning
multiple layer network and time series neural network
neural network
Biological neural networks
Recurrent neural networks
Issue Date: Apr-2017
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.
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.
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