447 research outputs found
LTI ODE-valued neural networks adaptation of the back propagation algorithm
In [Velasco et al., 2014], a new approach of the classical artificial neural network archi-tecture is introduced, named ’LTI ODE-valued neural networks’, whereLTI ODEstandsfor Linear Time Invariant Ordinal Differential Equation. In this novel system, nodes inthe artificial neural network are characterized by: inputs in the form of differentiablecontinuous-time signals; linear time-invariant ordinary differential equations (LTI ODE)as connection weights; and activation functions evaluated in the frequency domain.It was shown that this new configuration allows solving multiple problems at the sametime using a common neural structure. However, the article concludes with the need fordeveloping learning algorithms for the new model of neural network.Taking as starting point the drawback pointed out in [Velasco et al., 2014], the mainobjective of this master thesis is to develop a training algorithm for a LTI ODE-valuedneural network. As a first and natural approach, modifications of the BackPropagationalgorithm is considered as a general framework. Moreover, since the nature of the inputsare differentiable continuous-time signals, it is analyzed how to obtain a model that canbe physically implemented in the form of an analogical circui
A brief network analysis of Artificial Intelligence publication
In this paper, we present an illustration to the history of Artificial
Intelligence(AI) with a statistical analysis of publish since 1940. We
collected and mined through the IEEE publish data base to analysis the
geological and chronological variance of the activeness of research in AI. The
connections between different institutes are showed. The result shows that the
leading community of AI research are mainly in the USA, China, the Europe and
Japan. The key institutes, authors and the research hotspots are revealed. It
is found that the research institutes in the fields like Data Mining, Computer
Vision, Pattern Recognition and some other fields of Machine Learning are quite
consistent, implying a strong interaction between the community of each field.
It is also showed that the research of Electronic Engineering and Industrial or
Commercial applications are very active in California. Japan is also publishing
a lot of papers in robotics. Due to the limitation of data source, the result
might be overly influenced by the number of published articles, which is to our
best improved by applying network keynode analysis on the research community
instead of merely count the number of publish.Comment: 18 pages, 7 figure
An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis
We investigate the relationship between system identification and
intervention design in dynamical systems. While previous research demonstrated
how identifiable representation learning methods, such as Independent Component
Analysis (ICA), can reveal cause-effect relationships, it relied on a passive
perspective without considering how to collect data. Our work shows that in
Gaussian Linear Time-Invariant (LTI) systems, the system parameters can be
identified by introducing diverse intervention signals in a multi-environment
setting. By harnessing appropriate diversity assumptions motivated by the ICA
literature, our findings connect experiment design and representational
identifiability in dynamical systems. We corroborate our findings on synthetic
and (simulated) physical data. Additionally, we show that Hidden Markov Models,
in general, and (Gaussian) LTI systems, in particular, fulfil a generalization
of the Causal de Finetti theorem with continuous parameters.Comment: CLeaR2024 camera ready. Code available at
https://github.com/rpatrik96/lti-ic
NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations
This paper introduces Non-Autonomous Input-Output Stable Network (NAIS-Net),
a very deep architecture where each stacked processing block is derived from a
time-invariant non-autonomous dynamical system. Non-autonomy is implemented by
skip connections from the block input to each of the unrolled processing stages
and allows stability to be enforced so that blocks can be unrolled adaptively
to a pattern-dependent processing depth. NAIS-Net induces non-trivial,
Lipschitz input-output maps, even for an infinite unroll length. We prove that
the network is globally asymptotically stable so that for every initial
condition there is exactly one input-dependent equilibrium assuming tanh units,
and multiple stable equilibria for ReL units. An efficient implementation that
enforces the stability under derived conditions for both fully-connected and
convolutional layers is also presented. Experimental results show how NAIS-Net
exhibits stability in practice, yielding a significant reduction in
generalization gap compared to ResNets.Comment: NIPS 201
Multiple Faults Estimation in Dynamical Systems: Tractable Design and Performance Bounds
In this article, we propose a tractable nonlinear fault isolation filter
along with explicit performance bounds for a class of nonlinear dynamical
systems. We consider the presence of additive and multiplicative faults,
occurring simultaneously and through an identical dynamical relationship, which
represents a relevant case in several application domains. The proposed filter
architecture combines tools from model-based approaches in the control
literature and regression techniques from machine learning. To this end, we
view the regression operator through a system-theoretic perspective to develop
operator bounds that are then utilized to derive performance bounds for the
proposed estimation filter. In the case of constant, simultaneously and
identically acting additive and multiplicative faults, it can be shown that the
estimation error converges to zero with an exponential rate. The performance of
the proposed estimation filter in the presence of incipient faults is validated
through an application on the lateral safety systems of SAE level 4 automated
vehicles. The numerical results show that the theoretical bounds of this study
are indeed close to the actual estimation error.Comment: 24 pages, 8 figure
Robust identification of non-autonomous dynamical systems using stochastic dynamics models
This paper considers the problem of system identification (ID) of linear and
nonlinear non-autonomous systems from noisy and sparse data. We propose and
analyze an objective function derived from a Bayesian formulation for learning
a hidden Markov model with stochastic dynamics. We then analyze this objective
function in the context of several state-of-the-art approaches for both linear
and nonlinear system ID. In the former, we analyze least squares approaches for
Markov parameter estimation, and in the latter, we analyze the multiple
shooting approach. We demonstrate the limitations of the optimization problems
posed by these existing methods by showing that they can be seen as special
cases of the proposed optimization objective under certain simplifying
assumptions: conditional independence of data and zero model error.
Furthermore, we observe that our proposed approach has improved smoothness and
inherent regularization that make it well-suited for system ID and provide
mathematical explanations for these characteristics' origins. Finally,
numerical simulations demonstrate a mean squared error over 8.7 times lower
compared to multiple shooting when data are noisy and/or sparse. Moreover, the
proposed approach can identify accurate and generalizable models even when
there are more parameters than data or when the underlying system exhibits
chaotic behavior
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