85,680 research outputs found
SMGO: A Set Membership Approach to Data-Driven Global Optimization
Many science and engineering applications feature non-convex optimization
problems where the objective function can not be handled analytically, i.e. it
is a black box. Examples include design optimization via experiments, or via
costly finite elements simulations. To solve these problems, global
optimization routines are used. These iterative techniques must trade-off
exploitation close to the current best point with exploration of unseen regions
of the search space. In this respect, a new global optimization strategy based
on a Set Membership (SM) framework is proposed. Assuming Lipschitz continuity
of the cost function, the approach employs SM concepts to decide whether to
switch from an exploitation mode to an exploration one, and vice-versa. The
resulting algorithm, named SMGO (Set Membership Global Optimization) is
presented. Theoretical properties regarding convergence and computational
complexity are derived, and implementation aspects are discussed. Finally, the
SMGO performance is evaluated on a set of benchmark non-convex problems and
compared with those of other global optimization approaches
Learning-based predictive control for linear systems: a unitary approach
A comprehensive approach addressing identification and control for
learningbased Model Predictive Control (MPC) for linear systems is presented.
The design technique yields a data-driven MPC law, based on a dataset collected
from the working plant. The method is indirect, i.e. it relies on a model
learning phase and a model-based control design one, devised in an integrated
manner. In the model learning phase, a twofold outcome is achieved: first,
different optimal p-steps ahead prediction models are obtained, to be used in
the MPC cost function; secondly, a perturbed state-space model is derived, to
be used for robust constraint satisfaction. Resorting to Set Membership
techniques, a characterization of the bounded model uncertainties is obtained,
which is a key feature for a successful application of the robust control
algorithm. In the control design phase, a robust MPC law is proposed, able to
track piece-wise constant reference signals, with guaranteed recursive
feasibility and convergence properties. The controller embeds multistep
predictors in the cost function, it ensures robust constraints satisfaction
thanks to the learnt uncertainty model, and it can deal with possibly
unfeasible reference values. The proposed approach is finally tested in a
numerical example
Nonlinear Set Membership Regression with Adaptive Hyper-Parameter Estimation for Online Learning and Control.
Methods known as Lipschitz Interpolation or Nonlinear Set Membership regression have become established tools for nonparametric system-identification and data-based control. They utilise presupposed Lipschitz properties to compute inferences over unobserved function values. Unfortunately, it relies on the a priori knowledge of a Lipschitz constant
of the underlying target function which serves as a hyperparameter.
We propose a closed-form estimator of the Lipschitz constant that is robust to bounded observational noise in the data. The merger of Lipschitz Interpolation with the new hyperparameter estimator gives a new nonparametric machine learning method for which we derive sample complexity bounds and online learning convergence guarantees. Furthermore, we apply our learning method to model-reference adaptive control. We provide convergence guarantees on the closed-loop dynamics
and compare the performance of our approach to recently proposed alternative learning-based controllers in a simulated flight manoeuvre control scenario
Distributed interpolatory algorithms for set membership estimation
This work addresses the distributed estimation problem in a set membership
framework. The agents of a network collect measurements which are affected by
bounded errors, thus implying that the unknown parameters to be estimated
belong to a suitable feasible set. Two distributed algorithms are considered,
based on projections of the estimate of each agent onto its local feasible set.
The main contribution of the paper is to show that such algorithms are
asymptotic interpolatory estimators, i.e. they converge to an element of the
global feasible set, under the assumption that the feasible set associated to
each measurement is convex. The proposed techniques are demonstrated on a
distributed linear regression estimation problem
On the convergence of autonomous agent communities
This is the post-print version of the final published paper that is available from the link below. Copyright @ 2010 IOS Press and the authors.Community is a common phenomenon in natural ecosystems, human societies as well as artificial multi-agent systems such as those in web and Internet based applications. In many self-organizing systems, communities are formed evolutionarily in a decentralized way through agents' autonomous behavior. This paper systematically investigates the properties of a variety of the self-organizing agent community systems by a formal qualitative approach and a quantitative experimental approach. The qualitative formal study by applying formal specification in SLABS and Scenario Calculus has proven that mature and optimal communities always form and become stable when agents behave based on the collective knowledge of the communities, whereas community formation does not always reach maturity and optimality if agents behave solely based on individual knowledge, and the communities are not always stable even if such a formation is achieved. The quantitative experimental study by simulation has shown that the convergence time of agent communities depends on several parameters of the system in certain complicated patterns, including the number of agents, the number of community organizers, the number of knowledge categories, and the size of the knowledge in each category
Modeling heterogeneity in random graphs through latent space models: a selective review
We present a selective review on probabilistic modeling of heterogeneity in
random graphs. We focus on latent space models and more particularly on
stochastic block models and their extensions that have undergone major
developments in the last five years
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