Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
The dynamic universality of sigmoidal neural networks
Information and Computation
Analysis and design of echo state networks
Neural Computation
2007 Special Issue: The cerebellum as a liquid state machine
Neural Networks
A nonlinear prediction approach to the blind separation of convolutive mixtures
EURASIP Journal on Applied Signal Processing
A learning algorithm for continually running fully recurrent neural networks
Neural Computation
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Survey: Reservoir computing approaches to recurrent neural network training
Computer Science Review
Echo state networks for seasonal streamflow series forecasting
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
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Echo state networks (ESNs) can be interpreted as promoting an encouraging compromise between two seemingly conflicting objectives: (i) simplicity of the resulting mathematical model and (ii) capability to express a wide range of nonlinear dynamics. By imposing fixed weights to the recurrent connections, the echo state approach avoids the well-known difficulties faced by recurrent neural network training strategies, but still preserves, to a certain extent, the potential of the underlying structure due to the existence of feedback loops within the dynamical reservoir. Moreover, the overall training process is relatively simple, as it amounts essentially to adapting the readout, which usually corresponds to a linear combiner. However, the linear nature of the output layer may limit the capability of exploring the available information, since higher-order statistics of the signals are not taken into account. In this work, we present a novel architecture for an ESN in which the linear combiner is replaced by a Volterra filter structure. Additionally, the principal component analysis technique is used to reduce the number of effective signals transmitted to the output layer. This idea not only improves the processing capability of the network, but also preserves the simplicity of the training process. The proposed architecture is then analyzed in the context of a set of representative information extraction problems, more specifically supervised and unsupervised channel equalization, and blind separation of convolutive mixtures. The obtained results, when compared to those produced by already proposed ESN versions, highlight the benefits brought by the novel network proposal and characterize it as a promising tool to deal with challenging signal processing tasks.