-
As a consequence, more exact models of devices can
be retained for analysis rather than the APPROXIMATE models commonly introduced
for the sake of computational simplicity. A computer icon appears in the margin
with each introduction of MATLAB analysis.
標簽：
consequence
analysis
retained
approxi
上傳時間：
2016-04-07
上傳用戶：changeboy
-
As a consequence, more exact models of devices can
be retained for analysis rather than the APPROXIMATE models commonly introduced
for the sake of computational simplicity. A computer icon appears in the margin
with each introduction of MATLAB analysis.
標簽：
consequence
analysis
retained
approxi
上傳時間：
2014-01-15
上傳用戶：R50974
-
As a consequence, more exact models of devices can
be retained for analysis rather than the APPROXIMATE models commonly introduced
for the sake of computational simplicity. A computer icon appears in the margin
with each introduction of MATLAB analysis.
標簽：
consequence
analysis
retained
approxi
上傳時間：
2013-12-23
上傳用戶：czl10052678
-
GloptiPoly 3: moments, optimization and
semidefinite programming.
Gloptipoly 3 is intended to solve, or at least APPROXIMATE, the Generalized Problem of
Moments (GPM), an infinite-dimensional optimization problem which can be viewed as
an extension of the classical problem of moments [8]. From a theoretical viewpoint, the
GPM has developments and impact in various areas of mathematics such as algebra,
Fourier analysis, functional analysis, operator theory, probability and statistics, to cite
a few. In addition, and despite a rather simple and short formulation, the GPM has a
large number of important applications in various fields such as optimization, probability,
finance, control, signal processing, chemistry, cristallography, tomography, etc. For an
account of various methodologies as well as some of potential applications, the interested
reader is referred to [1, 2] and the nice collection of papers [5].
標簽：
optimization
semidefinite
programming
GloptiPoly
上傳時間：
2016-06-05
上傳用戶：lgnf
-
The package includes 3 Matlab-interfaces to the c-code:
1. inference.m
An interface to the full inference package, includes several methods for
APPROXIMATE inference: Loopy Belief Propagation, Generalized Belief
Propagation, Mean-Field approximation, and 4 monte-carlo sampling methods
(Metropolis, Gibbs, Wolff, Swendsen-Wang).
Use "help inference" from Matlab to see all options for usage.
2. gbp_preprocess.m and gbp.m
These 2 interfaces split Generalized Belief Propagation into the pre-process
stage (gbp_preprocess.m) and the inference stage (gbp.m), so the user may use
only one of them, or changing some parameters in between.
Use "help gbp_preprocess" and "help gbp" from Matlab.
3. simulatedAnnealing.m
An interface to the simulated-annealing c-code. This code uses Metropolis
sampling method, the same one used for inference.
Use "help simulatedAnnealing" from Matlab.
標簽：
Matlab-interfaces
inference
interface
the
上傳時間：
2016-08-27
上傳用戶：gxrui1991
-
The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the APPROXIMATE model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for APPROXIMATE identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order
標簽：
identification
considered
features
separati
上傳時間：
2016-09-20
上傳用戶：FreeSky
-
A Matlab toolbox for exact linear time-invariant system identification is presented. The emphasis is on the variety of possible ways to implement the mappings from data to parameters of the data generating system. The considered system representations are input/state/output, difference equation, and left matrix fraction.
KEYWORDS: subspace identification, deterministic subspace identification, balanced model reduction, APPROXIMATE system identification, MPUM.
標簽：
identification
time-invariant
presented
emphasis
上傳時間：
2013-12-28
上傳用戶：wfl_yy
-
This paper presents a visual based localization
mechanism for a legged robot. Our proposal, fundamented
on a probabilistic approach, uses a precompiled topological
map where natural landmarks like doors or ceiling lights
are recognized by the robot using its on-board camera.
Experiments have been conducted using the AIBO Sony
robotic dog showing that it is able to deal with noisy sensors
like vision and to APPROXIMATE world models representing
indoor ofce environments. The two major contributions of
this work are the use of this technique in legged robots, and
the use of an active camera as the main sensor
標簽：
localization
mechanism
presents
proposal
上傳時間：
2016-11-04
上傳用戶：dianxin61
-
A stability analysis is presented for staggered schemes for the governing equations of compressible flow. The
method is based on Fourier analysis. The APPROXIMATE nature of pressure-correction solution methods is taken into
account. 2001 IMACS. Published by Elsevier Science B.V. All rights reserved
標簽：
compressible
stability
for
equations
上傳時間：
2016-12-02
上傳用戶：yph853211
-
Basic function to locate and measure the positive peaks in a noisy
data sets. Detects peaks by looking for downward zero-crossings
in the smoothed third derivative that exceed SlopeThreshold
and peak amplitudes that exceed AmpThreshold. Determines,
position, height, and APPROXIMATE width of each peak by least-squares
curve-fitting the log of top part of the peak with a parabola.
標簽：
peaks
function
positive
Detects
上傳時間：
2017-04-26
上傳用戶：彭玖華
