一個(gè)使用免疫算法實(shí)現(xiàn)物流調(diào)度的源代碼，使用SQL Server 2005+Visual Studio C# 2005開發(fā)，可以在地圖上描出優(yōu)化路徑。壓縮包中的wldd.bak和wldd1.bak為數(shù)據(jù)庫文件，將其恢復(fù)到SQL Server 2005中即可。需要注意的是，默認(rèn)的連接字符串為
connectionString="Data Source=YANXL\SQLEXPRESS Initial Catalog=wldd1 User ID=sa Password=dream"
初始密碼可以改掉
This paper presents several low-latency mixed-timing
FIFO (first-in–first-out) interfaces designs that interface systems
on a chip working at different speeds. The connected systems
can be either synchronous or asynchronous. The designs are then
adapted to work between systems with very long interconnect
delays, by migrating a single-clock solution by Carloni et al.
(1999, 2000, and 2001) (for “l(fā)atency-insensitive” protocols) to
mixed-timing domains. The new designs can be made arbitrarily
robust with regard to metastability and interface operating speeds.
Initial simulations for both latency and throughput are promising.
#if !defined(AFX_GAQUEEN_H__C26AE0A3_F9B4_426F_A324_B460CC7946CB__INCLUDED_)
#define AFX_GAQUEEN_H__C26AE0A3_F9B4_426F_A324_B460CC7946CB__INCLUDED_
#if _MSC_VER > 1000
#pragma once
#endif // _MSC_VER > 1000
class CGAQueen
{
public:
CGAQueen(int nPopulation,int nIteration,float Mutation,int mChBoard)
virtual ~CGAQueen()
void Clear() // to clear chess board with 0 value
void InitialPopulation() // to create the first and Initial randompopulation
void FillArea(int index) // to fill chess board with desired chromosome
int CostFunc(int index) // determine the cost of matrix[index][index]
void PopulationSort() // to sort population from the best to the worst
void GenerateCrossOverMatrix() // a way to create children from parent is CcrossOver
void Mating() // to create children from parents
void Ap
How the K-mean Cluster work
Step 1. Begin with a decision the value of k = number of clusters
Step 2. Put any Initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following:
Take the first k training sample as single-element clusters
Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster.
Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample.
Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.
更新內(nèi)容： 1 增加了搜索功能 2 提供了幫助頁面 修改若干小問題 管理名稱 admin 初始密碼：admin-update : added a search function to help provide two pages of small amendments to a number of issues management Initial name admin Password : admin
ET++ is a portable and homogenous object-oriented class library integrating user
interface building blocks, basic data structures, and high level application framework
components. ET++ eases the building of highly interactive applications with consistent
user interfaces following the direct manipulation principle. The ET++ class library is
implemented in C++ and can be used on several operating systems and window system
platforms. Since its Initial conception the class library has been continuously
redesigned and improved. It started with an architecture which was close to MacApp.
During several iterations a new and unique architecture evolved. A byproduct of the
ET++ project is a set of tools, which were designed to support the exploration of ET++
applications at run-time.
設(shè)計(jì)模式一書引用的主要參考例程，一個(gè)跨平臺(tái)的應(yīng)用框架，基于C++實(shí)現(xiàn)，是學(xué)習(xí)面向?qū)ο蟮慕?jīng)典源碼.
runs Kalman-Bucy filter over observations matrix Z
for 1-step prediction onto matrix X (X can = Z)
with model order p
V = Initial covariance of observation sequence noise
returns model parameter estimation sequence A,
sequence of predicted outcomes y_pred
and error matrix Ey (reshaped) for y and Ea for a
along with inovation prob P = P(y_t | D_t-1) = evidence
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.
