C/C++ implementation of the Levenberg-Marquardt non-linear
least squares algorithm. levmar includes double and single precision LM versions, both
with analytic and finite difference approximated jacobians
T-kernel 的extension源代碼，是日本最著名的T-kernel所獨有的，適合開發T-kernel的朋友們使用！
TK/SE is the program that extends T-Kernel and provides the functions such as a file system and a process management.
The TK/SE archive to be provided is comprised of the main portion and the 2 extended file system portions, and TK/SE object is built by adding these to T-Kernel source.
a) tkernel_se_1.00.00.tar.gz Main source of T-Kernel/SE
b) extfs_fatfs_1.00.00.tar.gz difference source of T-Kernel/SE extended file system (FAT)
c) extfs_cdrom_1.00.00.tar.gz difference source of T-Kernel/SE extended file system (CD-ROM)
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
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.
The inverse of the gradient function. I ve provided versions that work on 1-d vectors, or 2-d or 3-d arrays. In the 1-d case I offer 5 different methods, from cumtrapz, and an integrated cubic spline, plus several finite difference methods.
In higher dimensions, only a finite difference/linear algebra solution is provided, but it is fully vectorized and fully sparse in its approach. In 2-d and 3-d, if the gradients are inconsistent, then a least squares solution is generated
C programming is a craft that takes years to perfect. A reasonably sharp person can learn the basics of
C quite quickly. But it takes much longer to master the nuances of the language and to write enough
programs, and enough different programs, to become an expert. In natural language terms, this is the
difference between being able to order a cup of coffee in Paris, and (on the Metro) being able to tell anative Parisienne where to get off. This book is an advanced text on the ANSI C programming
language. It is intended for people who are already writing C programs, and who want to quickly pick
up some of the insights and techniques of experts.
Guided vehicles (GVs) are commonly used for the internal transportation of loads in warehouses, production plants and terminals. These guided vehicles can be routed with a variety of vehicle dispatching rules in an attempt to meet performance criteria such as minimizing the average load waiting times. In this research, we use simulation models of three companies to evaluate the performance of several real-time vehicle dispatching rules, in part described in the literature. It appears that there
is a clear difference in average load waiting time between the different dispatching rules in the different environments. Simple rules, based on load and vehicle proximity (distance-based) perform best for all cases. The penalty for this is a relatively high maximum load waiting time. A distance-based rule with time truncation, giving more priority to loads that have to wait longer than a time threshold, appears to yield the best possible overall performance. A rule that particularly considers load-waiting time performs poor overall. We also show that using little pre-arrival information of loads leads to a significant improvement in the performance of the dispatching rules without changing their performance ranking.
We consider the problem of target localization by a
network of passive sensors. When an unknown target emits an
acoustic or a radio signal, its position can be localized with multiple
sensors using the time difference of arrival (TDOA) information.
In this paper, we consider the maximum likelihood formulation
of this target localization problem and provide efficient convex
relaxations for this nonconvex optimization problem.We also propose
a formulation for robust target localization in the presence of
sensor location errors. Two Cramer-Rao bounds are derived corresponding
to situations with and without sensor node location errors.
Simulation results confirm the efficiency and superior performance
of the convex relaxation approach as compared to the
existing least squares based approach when large sensor node location
errors are present.
The CommScope InstaPATCH? 360 and ReadyPATCH? solutions utilize a
standards-compliant multi-fiber connector to provide high density termination
capability. The connector is called an MPO (Multi-fiber Push On) connector by
the standards. In many cases, multi-fiber connector products are referred to as
MTP connectors. This document is intended to clarify the difference between the two terms – MPO and MTP.
