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 toolbox solves a variety of approximate modeling problems for linear static models. The model can be parameterized in kernel, image, or input/output form and the approximation criterion, called misfit, is a weighted norm between the given data and data that is consistent with the model. There are three main classes of functions in the toolbox: transformation functions, misfit computation functions, and approximation functions. The approximation functions derive an approximate model from data, the misfit computation functions are used for validation and comparison of models, and the transformation functions are used for deriving one model representation from another.
KEYWORDS: Total least squares, generalized total least squares, software implementation.
FCP takes a file, generates a random 2048 bit key and encrypts the file with
a RC4 stream cipher. The encrypted file is written to a new file along with
the decryption stub and key. When the output file is executed it decrypts and
executes the encrypted file.
It s written in Delphi 6, enjoy the source code.
CPU的code banking技術實例：
This Zip file contains five (3) folders:
FastChip Project Files
* This folder contains a folder called "Bank" that
should be moved into:
<install_root>\FastChip\Projects
Keil Project Files
* These files are to be put into the directory of
your choice and the project is to be opened from
within Keil
Hex Files
* These files are the output of Keil. If you do not
want to compile and link all of the code, these files
can be loaded into FastChip directly
Export a vertices/faces patch to an STL triangular mesh.This is based heavily on Bill McDonald s previous work, simply enabling his output functions for a different form of input.
內存管理程序，功能與FASTMM相似，PLEASE NOTE: There are two ways to install BigBrain. You may use the
memory manager code natively compiled into your EXE or you can use
an included external DLL which will allow you to share memory across
multiple DLLs with one central place for memory management. Using the DLL
allows your application to share strings, and serves the same purpose
as the ShareMem unit included with Delphi. BigBrainShareMem.dll should
be 100％ compatible with the DelphiMM.dll and could even simply be renamed
to DelphiMM.dll to simplify deployment.
