32 research outputs found
Empirical Analysis of the Necessary and Sufficient Conditions of the Echo State Property
The Echo State Network (ESN) is a specific recurrent network, which has
gained popularity during the last years. The model has a recurrent network
named reservoir, that is fixed during the learning process. The reservoir is
used for transforming the input space in a larger space. A fundamental property
that provokes an impact on the model accuracy is the Echo State Property (ESP).
There are two main theoretical results related to the ESP. First, a sufficient
condition for the ESP existence that involves the singular values of the
reservoir matrix. Second, a necessary condition for the ESP. The ESP can be
violated according to the spectral radius value of the reservoir matrix. There
is a theoretical gap between these necessary and sufficient conditions. This
article presents an empirical analysis of the accuracy and the projections of
reservoirs that satisfy this theoretical gap. It gives some insights about the
generation of the reservoir matrix. From previous works, it is already known
that the optimal accuracy is obtained near to the border of stability control
of the dynamics. Then, according to our empirical results, we can see that this
border seems to be closer to the sufficient conditions than to the necessary
conditions of the ESP.Comment: 23 pages, 14 figures, accepted paper for the IEEE IJCNN, 201
Sparsity in Reservoir Computing Neural Networks
Reservoir Computing (RC) is a well-known strategy for designing Recurrent
Neural Networks featured by striking efficiency of training. The crucial aspect
of RC is to properly instantiate the hidden recurrent layer that serves as
dynamical memory to the system. In this respect, the common recipe is to create
a pool of randomly and sparsely connected recurrent neurons. While the aspect
of sparsity in the design of RC systems has been debated in the literature, it
is nowadays understood mainly as a way to enhance the efficiency of
computation, exploiting sparse matrix operations. In this paper, we empirically
investigate the role of sparsity in RC network design under the perspective of
the richness of the developed temporal representations. We analyze both
sparsity in the recurrent connections, and in the connections from the input to
the reservoir. Our results point out that sparsity, in particular in
input-reservoir connections, has a major role in developing internal temporal
representations that have a longer short-term memory of past inputs and a
higher dimension.Comment: This paper is currently under revie
A Comparative Study of Reservoir Computing for Temporal Signal Processing
Reservoir computing (RC) is a novel approach to time series prediction using
recurrent neural networks. In RC, an input signal perturbs the intrinsic
dynamics of a medium called a reservoir. A readout layer is then trained to
reconstruct a target output from the reservoir's state. The multitude of RC
architectures and evaluation metrics poses a challenge to both practitioners
and theorists who study the task-solving performance and computational power of
RC. In addition, in contrast to traditional computation models, the reservoir
is a dynamical system in which computation and memory are inseparable, and
therefore hard to analyze. Here, we compare echo state networks (ESN), a
popular RC architecture, with tapped-delay lines (DL) and nonlinear
autoregressive exogenous (NARX) networks, which we use to model systems with
limited computation and limited memory respectively. We compare the performance
of the three systems while computing three common benchmark time series:
H{\'e}non Map, NARMA10, and NARMA20. We find that the role of the reservoir in
the reservoir computing paradigm goes beyond providing a memory of the past
inputs. The DL and the NARX network have higher memorization capability, but
fall short of the generalization power of the ESN
Smart environments and context-awareness for lifestyle management in a healthy active ageing framework
Health trends of elderly in Europe motivate the need for technological solutions aimed at preventing the main causes of morbidity and premature mortality. In this framework, the DOREMI project addresses three important causes of morbidity and mortality in the elderly by devising an ICT-based home care services for aging people to contrast cognitive decline, sedentariness and unhealthy dietary habits. In this paper, we present the general architecture of DOREMI, focusing on its aspects of human activity recognition and reasoning