302 research outputs found
Bidirectional deep-readout echo state networks
We propose a deep architecture for the classification of multivariate time
series. By means of a recurrent and untrained reservoir we generate a vectorial
representation that embeds temporal relationships in the data. To improve the
memorization capability, we implement a bidirectional reservoir, whose last
state captures also past dependencies in the input. We apply dimensionality
reduction to the final reservoir states to obtain compressed fixed size
representations of the time series. These are subsequently fed into a deep
feedforward network trained to perform the final classification. We test our
architecture on benchmark datasets and on a real-world use-case of blood
samples classification. Results show that our method performs better than a
standard echo state network and, at the same time, achieves results comparable
to a fully-trained recurrent network, but with a faster training
Short-term Memory of Deep RNN
The extension of deep learning towards temporal data processing is gaining an
increasing research interest. In this paper we investigate the properties of
state dynamics developed in successive levels of deep recurrent neural networks
(RNNs) in terms of short-term memory abilities. Our results reveal interesting
insights that shed light on the nature of layering as a factor of RNN design.
Noticeably, higher layers in a hierarchically organized RNN architecture
results to be inherently biased towards longer memory spans even prior to
training of the recurrent connections. Moreover, in the context of Reservoir
Computing framework, our analysis also points out the benefit of a layered
recurrent organization as an efficient approach to improve the memory skills of
reservoir models.Comment: This is a pre-print (pre-review) version of the paper accepted for
presentation at the 26th European Symposium on Artificial Neural Networks,
Computational Intelligence and Machine Learning (ESANN), Bruges (Belgium),
25-27 April 201
System Identification of multi-rotor UAVs using echo state networks
Controller design for aircraft with unusual configurations presents unique challenges, particularly in extracting valid mathematical models of the MRUAVs behaviour. System Identification is a collection of techniques for extracting an accurate mathematical model of a dynamic system from experimental input-output data. This can entail parameter identification only (known as grey-box modelling) or more generally full parameter/structural identification of the nonlinear mapping (known as black-box). In this paper we propose a new method for black-box identification of the non-linear dynamic model of a small MRUAV using Echo State Networks (ESN), a novel approach to train Recurrent Neural Networks (RNN)
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