35,701 research outputs found
Hybrid optimization method with general switching strategy for parameter estimation
This article is available from: http://www.biomedcentral.com/1752-0509/2/26[Background] Modeling and simulation of cellular signaling and metabolic pathways as networks of
biochemical reactions yields sets of non-linear ordinary differential equations. These models usually
depend on several parameters and initial conditions. If these parameters are unknown, results from
simulation studies can be misleading. Such a scenario can be avoided by fitting the model to
experimental data before analyzing the system. This involves parameter estimation which is usually
performed by minimizing a cost function which quantifies the difference between model predictions
and measurements. Mathematically, this is formulated as a non-linear optimization problem which
often results to be multi-modal (non-convex), rendering local optimization methods detrimental.[Results] In this work we propose a new hybrid global method, based on the combination of an
evolutionary search strategy with a local multiple-shooting approach, which offers a reliable and
efficient alternative for the solution of large scale parameter estimation problems.[Conclusion] The presented new hybrid strategy offers two main advantages over previous
approaches: First, it is equipped with a switching strategy which allows the systematic
determination of the transition from the local to global search. This avoids computationally
expensive tests in advance. Second, using multiple-shooting as the local search procedure reduces
the multi-modality of the non-linear optimization problem significantly. Because multiple-shooting
avoids possible spurious solutions in the vicinity of the global optimum it often outperforms the
frequently used initial value approach (single-shooting). Thereby, the use of multiple-shooting yields
an enhanced robustness of the hybrid approach.This work was supported by the European Community as part of the FP6
COSBICS Project (STREP FP6-512060), the German Federal Ministry of
Education and Research, BMBF-project FRISYS (grant 0313921) and Xunta
de Galicia (PGIDIT05PXIC40201PM).Peer reviewe
Global optimization for low-dimensional switching linear regression and bounded-error estimation
The paper provides global optimization algorithms for two particularly
difficult nonconvex problems raised by hybrid system identification: switching
linear regression and bounded-error estimation. While most works focus on local
optimization heuristics without global optimality guarantees or with guarantees
valid only under restrictive conditions, the proposed approach always yields a
solution with a certificate of global optimality. This approach relies on a
branch-and-bound strategy for which we devise lower bounds that can be
efficiently computed. In order to obtain scalable algorithms with respect to
the number of data, we directly optimize the model parameters in a continuous
optimization setting without involving integer variables. Numerical experiments
show that the proposed algorithms offer a higher accuracy than convex
relaxations with a reasonable computational burden for hybrid system
identification. In addition, we discuss how bounded-error estimation is related
to robust estimation in the presence of outliers and exact recovery under
sparse noise, for which we also obtain promising numerical results
A view of Estimation of Distribution Algorithms through the lens of Expectation-Maximization
We show that a large class of Estimation of Distribution Algorithms,
including, but not limited to, Covariance Matrix Adaption, can be written as a
Monte Carlo Expectation-Maximization algorithm, and as exact EM in the limit of
infinite samples. Because EM sits on a rigorous statistical foundation and has
been thoroughly analyzed, this connection provides a new coherent framework
with which to reason about EDAs
Safety Barrier Certificates for Heterogeneous Multi-Robot Systems
This paper presents a formal framework for collision avoidance in multi-robot
systems, wherein an existing controller is modified in a minimally invasive
fashion to ensure safety. We build this framework through the use of control
barrier functions (CBFs) which guarantee forward invariance of a safe set;
these yield safety barrier certificates in the context of heterogeneous robot
dynamics subject to acceleration bounds. Moreover, safety barrier certificates
are extended to a distributed control framework, wherein neighboring agent
dynamics are unknown, through local parameter identification. The end result is
an optimization-based controller that formally guarantees collision free
behavior in heterogeneous multi-agent systems by minimally modifying the
desired controller via safety barrier constraints. This formal result is
verified in simulation on a multi-robot system consisting of both cumbersome
and agile robots, is demonstrated experimentally on a system with a Magellan
Pro robot and three Khepera III robots.Comment: 8 pages version of 2016ACC conference paper, experimental results
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Bat Algorithm: Literature Review and Applications
Bat algorithm (BA) is a bio-inspired algorithm developed by Yang in 2010 and
BA has been found to be very efficient. As a result, the literature has
expanded significantly in the last 3 years. This paper provides a timely review
of the bat algorithm and its new variants. A wide range of diverse applications
and case studies are also reviewed and summarized briefly here. Further
research topics are also discussed.Comment: 10 page
Estimation and prediction of road traffic flow using particle filter for real-time traffic control
Real-data testing results of a real-time state estimator and predictor are presented with particular focus on the feature of enabling of detector fault alarms and also its relation to queue-length based traffic control. A parameter and state estimator/predictor is developed by using particle filter. The simulation testing results are quite satisfactory and promising for further work on developing a hybrid model of traffic flow that captures the transition between low and high intensity. By using this hybrid model, it may be more feasible to achieve the significant feature of automatic adaptation to changing system condition
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