RFID標準：iso18000-6（英文版）。
Information technology — Radio-frequency identification for item management — Part 6: Parameters for air interface communications at 860 MHz to 960 MHz
Swarm intelligence algorithms are based on natural
behaviors. Particle swarm optimization (PSO) is a
stochastic search and optimization tool. Changes in the
PSO Parameters, namely the inertia weight and the
cognitive and social acceleration constants, affect the
performance of the search process. This paper presents a
novel method to dynamically change the values of these
Parameters during the search. Adaptive critic design
(ACD) has been applied for dynamically changing the
values of the PSO Parameters.
多項式曲線擬合 任意介數(shù) Purpose - Least-squares curve fit of arbitrary order
working in C++ Builder 2007 as a template class,
using vector<FloatType> Parameters.
Added a method to handle some EMathError exceptions.
If do NOT want to use this just call PolyFit2 directly.
usage: Call PolyFit by something like this.
CPolyFit<double> PolyFitObj
double correlation_coefficiant = PolyFitObj.PolyFit(X, Y, A)
where X and Y are vectors of doubles which must have the same size and
A is a vector of doubles which must be the same size as the number of
coefficients required.
returns: The correlation coefficient or -1 on failure.
produces: A vector (A) which holds the coefficients.
On-Line MCMC Bayesian Model Selection
This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model Parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation Parameters.
This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model Parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation Parameters.
This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model Parameters, regularisation Parameters and noise Parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar -xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation Parameters.
％％％ Demos for PUMA algorithms ％％％
We present four matlab demos for PUMA. demo1, demo2, demo3, and demo4
illustrate PUMA working with different Parameters and with four
different images.
All you need to do is to run each of the demos. Please be sure that
all the files are put on an accessible path for matlab.
Notice that this code is intended for research purposes only.
For further reference see "Phase Unwrapping via Graph Cuts,
IEEE Transactions on Image Processing, 2007
A technical trading system comprises a set of trading rules that can be used to generate trading signals. In general, a simple trading system has one or two Parameters that determine the timing of trading signals. Each rule contained in a trading system is the results of parameterizations.
(Source: The Profitability of Technical Analysis: A Review by Cheol-Ho Park and Scott H. Irwin)
This folder has some scritps that you may find usefull.
All of it comes from questions that I ve received in my email.
If you have a new request/question, feel free to send it to marceloperlin@gmail.com.
But please, don t ask me to do your homework.
Passing_your_param0
This folder contains instructions (and m files) for passing you own initial Parameters to the fitting function.
I also included a simple simulation script that will create random initial coefficients
(under the proper bounds) and fit the model to the data.
linux下用C語言寫的聊天程序！/*BUG and NOTE:
Not join protect to Shared Memory Segments,example Semaphore Arrays.
Not check the Parameters validity.
When transmit may be error.
If the client program no right exit,others cannot know,the service program shouldbe check the client program whether exist timing.
The program when exit(), it do not delete the Shared Memory Segments,you maybe use atexit() but the program have some processes.
When one process is exit(),others is continue.
The service program s action is few.
And so on. */
