2,424 research outputs found
Training Echo State Networks with Regularization through Dimensionality Reduction
In this paper we introduce a new framework to train an Echo State Network to
predict real valued time-series. The method consists in projecting the output
of the internal layer of the network on a space with lower dimensionality,
before training the output layer to learn the target task. Notably, we enforce
a regularization constraint that leads to better generalization capabilities.
We evaluate the performances of our approach on several benchmark tests, using
different techniques to train the readout of the network, achieving superior
predictive performance when using the proposed framework. Finally, we provide
an insight on the effectiveness of the implemented mechanics through a
visualization of the trajectory in the phase space and relying on the
methodologies of nonlinear time-series analysis. By applying our method on well
known chaotic systems, we provide evidence that the lower dimensional embedding
retains the dynamical properties of the underlying system better than the
full-dimensional internal states of the network
A Bayesian approach to simultaneously characterize the stochastic and deterministic components of a system
The present work provides a Bayesian approach to learn plausible models capable of characterizing complex time series in which deterministic and stochastic phenomena concur. Two main approaches are actually developed. The first approach, is a simple superposition model grounded on the hypothesis that the interactions between the stochastic and deterministic phenomena are negligible. To enable this model to capture complex dynamics, the stochastic part is assumed to be a fractal signal. Under the assumptions of this model, an analysis method is proposed, enabling the characterization of the fractal stochastic component and the estimation the deterministic part.
The second main approach relies on Stochastic Differential Equations (SDEs) to model systems where the stochastic and deterministic part interact. First, a non-parametric estimation method for SDEs is developed, using recent advances from Gaussian processes. Finally, the thesis studies how to overcome the main constraint that the use of SDEs imposes: the Markovianity assumption. To that end, a new structured variational autoencoder with latent SDE dynamics is proposed.
All the methods are tested on both synthetic and real signals, demonstrating its ability to capture the behavior of complex systems
Intrinsic Dimension Estimation: Relevant Techniques and a Benchmark Framework
When dealing with datasets comprising high-dimensional points, it is usually advantageous to discover some data structure. A fundamental information needed to this aim is the minimum number of parameters required to describe the data while minimizing the information loss. This number, usually called intrinsic dimension, can be interpreted as the dimension of the manifold from which the input data are supposed to be drawn. Due to its usefulness in many theoretical and practical problems, in the last decades the concept of intrinsic dimension has gained considerable attention in the scientific community, motivating the large number of intrinsic dimensionality estimators proposed in the literature. However, the problem is still open since most techniques cannot efficiently deal with datasets drawn from manifolds of high intrinsic dimension and nonlinearly embedded in higher dimensional spaces. This paper surveys some of the most interesting, widespread used, and advanced state-of-the-art methodologies. Unfortunately, since no benchmark database exists in this research field, an objective comparison among different techniques is not possible. Consequently, we suggest a benchmark framework and apply it to comparatively evaluate relevant state-of-the-art estimators
Analysis of Embodied and Situated Systems from an Antireductionist Perspective
The analysis of embodied and situated agents form a dynamical system perspective is often
limited to a geometrical and qualitative description. However, a quantitative analysis is necessary
to achieve a deep understanding of cognitive facts.
The field of embodied cognition is multifaceted, and the first part of this thesis is devoted to exploring
the diverse meanings proposed in the existing literature. This is a preliminary fundamental
step as the creation of synthetic models requires well-founded theoretical and foundational
boundaries for operationalising the concept of embodied and situated cognition in a concrete
neuro-robotic model. By accepting the dynamical system view the agent is conceived as highly
integrated and strictly coupled with the surrounding environment. Therefore the antireductionist
framework is followed during the analysis of such systems, using chaos theory to unveil global
properties and information theory to describe the complex network of interactions among the
heterogeneous sub-components.
In the experimental section, several evolutionary robotics experiments are discussed. This class
of adaptive systems is consistent with the proposed definition of embodied and situated cognition.
In fact, such neuro-robotics platforms autonomously develop a solution to a problem exploiting
the continuous sensorimotor interaction with the environment.
The first experiment is a stress test for chaos theory, a mathematical framework that studies erratic
behaviour in low-dimensional and deterministic dynamical systems. The recorded dataset
consists of the robots’ position in the environment during the execution of the task. Subsequently,
the time series is projected onto a multidimensional phase space in order to study the underlying
dynamic using chaotic numerical descriptors. Finally, such measures are correlated and confronted
with the robots’ behavioural strategy and the performance in novel and unpredictable
environments.
The second experiment explores the possible applications of information-theoretic measures for
the analysis of embodied and situated systems. Data is recorded from perceptual and motor
neurons while robots are executing a wall-following task and pairwise estimations of the mutual
information and the transfer entropy are calculated in order to create an exhaustive map of the
nonlinear interactions among variables. Results show that the set of information-theoretic employed
in this study unveils characteristics of the agent-environemnt interaction and the functional
neural structure.
This work aims at testing the explanatory power and impotence of nonlinear time series analysis
applied to observables recorded from neuro-robotics embodied and situated systems
Variational Downscaling, Fusion and Assimilation of Hydrometeorological States via Regularized Estimation
Improved estimation of hydrometeorological states from down-sampled
observations and background model forecasts in a noisy environment, has been a
subject of growing research in the past decades. Here, we introduce a unified
framework that ties together the problems of downscaling, data fusion and data
assimilation as ill-posed inverse problems. This framework seeks solutions
beyond the classic least squares estimation paradigms by imposing proper
regularization, which are constraints consistent with the degree of smoothness
and probabilistic structure of the underlying state. We review relevant
regularization methods in derivative space and extend classic formulations of
the aforementioned problems with particular emphasis on hydrologic and
atmospheric applications. Informed by the statistical characteristics of the
state variable of interest, the central results of the paper suggest that
proper regularization can lead to a more accurate and stable recovery of the
true state and hence more skillful forecasts. In particular, using the Tikhonov
and Huber regularization in the derivative space, the promise of the proposed
framework is demonstrated in static downscaling and fusion of synthetic
multi-sensor precipitation data, while a data assimilation numerical experiment
is presented using the heat equation in a variational setting
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