1,543 research outputs found
Tests for conditional heteroscedasticity of functional data
Functional data objects derived from high-frequency financial data often exhibit volatility clustering. Versions of functional generalized autoregressive conditionally heteroscedastic (FGARCH) models have recently been proposed to describe such data, however so far basic diagnostic tests for these models are not available. We propose two portmanteau type tests to measure conditional heteroscedasticity in the squares of asset return curves. A complete asymptotic theory is provided for each test. We also show how such tests can be adapted and applied to model residuals to evaluate adequacy, and inform order selection, of FGARCH models. Simulation results show that both tests have good size and power to detect conditional heteroscedasticity and model mis-specification in finite samples. In an application, the tests show that intra-day asset return curves exhibit conditional heteroscedasticity. This conditional heteroscedasticity cannot be explained by the magnitude of inter-daily returns alone, but it can be adequately modeled by an FGARCH(1,1) model
Extracting dynamical equations from experimental data is NP-hard
The behavior of any physical system is governed by its underlying dynamical
equations. Much of physics is concerned with discovering these dynamical
equations and understanding their consequences. In this work, we show that,
remarkably, identifying the underlying dynamical equation from any amount of
experimental data, however precise, is a provably computationally hard problem
(it is NP-hard), both for classical and quantum mechanical systems. As a
by-product of this work, we give complexity-theoretic answers to both the
quantum and classical embedding problems, two long-standing open problems in
mathematics (the classical problem, in particular, dating back over 70 years).Comment: For mathematical details, see arXiv:0908.2128[math-ph]. v2: final
version, accepted in Phys. Rev. Let
Prediction and Simulator Verification of Roll/Lateral Adverse Aeroservoelastic Rotorcraft–Pilot Couplings
The involuntary interaction of a pilot with an aircraft can be described as pilot-assisted oscillations. Such
phenomena are usually only addressed late in the design process when they manifest themselves during ground/flight
testing. Methods to be able to predict such phenomena as early as possible are therefore useful. This work describes a
technique to predict the adverse aeroservoelastic rotorcraft–pilot couplings, specifically between a rotorcraft’s roll
motion and the resultant involuntary pilot lateral cyclic motion. By coupling linear vehicle aeroservoelastic models
and experimentally identified pilot biodynamic models, pilot-assisted oscillations and no-pilot-assisted oscillation
conditions have been numerically predicted for a soft-in-plane hingeless helicopter with a lightly damped regressive
lead–lag mode that strongly interacts with the roll modeat a frequency within the biodynamic band of the pilots. These
predictions have then been verified using real-time flight-simulation experiments. The absence of any similar adverse
couplings experienced while using only rigid-body models in the flight simulator verified that the observed
phenomena were indeed aeroelastic in nature. The excellent agreement between the numerical predictions and the
observed experimental results indicates that the techniques developed in this paper can be used to highlight the
proneness of new or existing designs to pilot-assisted oscillation
Neural Modeling and Control of Diesel Engine with Pollution Constraints
The paper describes a neural approach for modelling and control of a
turbocharged Diesel engine. A neural model, whose structure is mainly based on
some physical equations describing the engine behaviour, is built for the
rotation speed and the exhaust gas opacity. The model is composed of three
interconnected neural submodels, each of them constituting a nonlinear
multi-input single-output error model. The structural identification and the
parameter estimation from data gathered on a real engine are described. The
neural direct model is then used to determine a neural controller of the
engine, in a specialized training scheme minimising a multivariable criterion.
Simulations show the effect of the pollution constraint weighting on a
trajectory tracking of the engine speed. Neural networks, which are flexible
and parsimonious nonlinear black-box models, with universal approximation
capabilities, can accurately describe or control complex nonlinear systems,
with little a priori theoretical knowledge. The presented work extends optimal
neuro-control to the multivariable case and shows the flexibility of neural
optimisers. Considering the preliminary results, it appears that neural
networks can be used as embedded models for engine control, to satisfy the more
and more restricting pollutant emission legislation. Particularly, they are
able to model nonlinear dynamics and outperform during transients the control
schemes based on static mappings.Comment: 15 page
Experimental Results of Concurrent Learning Adaptive Controllers
Commonly used Proportional-Integral-Derivative based UAV flight controllers are often seen to provide adequate trajectory-tracking performance only after extensive tuning. The gains of these controllers are tuned to particular platforms, which makes transferring controllers from one UAV to other time-intensive. This paper suggests the use of adaptive controllers in speeding up the process of extracting good control performance from new UAVs. In particular, it is shown that a concurrent learning adaptive controller improves the trajectory tracking performance of a quadrotor with baseline linear controller directly imported from another quadrotors whose inertial characteristics and throttle mapping are very di fferent. Concurrent learning adaptive control uses specifi cally selected and online recorded data concurrently with instantaneous data and is capable of guaranteeing tracking error and weight error convergence without requiring persistency of excitation. Flight-test results are presented on indoor quadrotor platforms operated in MIT's RAVEN environment. These results indicate the feasibility of rapidly developing high-performance UAV controllers by using adaptive control to augment a controller transferred from another UAV with similar control assignment structure.United States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N000141110688)National Science Foundation (U.S.). Graduate Research Fellowship Program (Grant 0645960)Boeing Scientific Research Laboratorie
Thermal diagnostic of the Optical Window on board LISA Pathfinder
Vacuum conditions inside the LTP Gravitational Reference Sensor must comply
with rather demanding requirements. The Optical Window (OW) is an interface
which seals the vacuum enclosure and, at the same time, lets the laser beam go
through for interferometric Metrology with the test masses. The OW is a
plane-parallel plate clamped in a Titanium flange, and is considerably
sensitive to thermal and stress fluctuations. It is critical for the required
precision measurements, hence its temperature will be carefully monitored in
flight. This paper reports on the results of a series of OW characterisation
laboratory runs, intended to study its response to selected thermal signals, as
well as their fit to numerical models, and the meaning of the latter. We find
that a single pole ARMA transfer function provides a consistent approximation
to the OW response to thermal excitations, and derive a relationship with the
physical processes taking place in the OW. We also show how system noise
reduction can be accomplished by means of that transfer function.Comment: 20 pages, 14 figures; accepted for publication in Class. Quantum Gra
Classification Of Breast Lesions Using Artificial Neural Network.
This paper presents a study on classification of breast lesions using artificial neural network. Thirteen morphological features have been extracted from breast lesion cells and used as the neural network inputs for the classification
Global parameter identification of stochastic reaction networks from single trajectories
We consider the problem of inferring the unknown parameters of a stochastic
biochemical network model from a single measured time-course of the
concentration of some of the involved species. Such measurements are available,
e.g., from live-cell fluorescence microscopy in image-based systems biology. In
addition, fluctuation time-courses from, e.g., fluorescence correlation
spectroscopy provide additional information about the system dynamics that can
be used to more robustly infer parameters than when considering only mean
concentrations. Estimating model parameters from a single experimental
trajectory enables single-cell measurements and quantification of cell--cell
variability. We propose a novel combination of an adaptive Monte Carlo sampler,
called Gaussian Adaptation, and efficient exact stochastic simulation
algorithms that allows parameter identification from single stochastic
trajectories. We benchmark the proposed method on a linear and a non-linear
reaction network at steady state and during transient phases. In addition, we
demonstrate that the present method also provides an ellipsoidal volume
estimate of the viable part of parameter space and is able to estimate the
physical volume of the compartment in which the observed reactions take place.Comment: Article in print as a book chapter in Springer's "Advances in Systems
Biology
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