zhengyundong

说明：  本文针对无线网络中的多种服务类型，对 无线网络的带宽分配和接入方式进行分析，深入分析了不同类型的网络带宽资源 分配和调度算法在运行过程中对网络性能的影响。同时，本文也针对切换服务对 无线网络带宽分配的影响情况进行了分析，对切换服务的路由重建方式进行优 化，提高网络的实际运行性能。(This paper has analyzed the bandwidth allocating process of wireless network with different services types and compared the performance of different bandwidth allocating schemes. At the same time, this paper also gives an eye to the impact brought by handoff process. We have optimized the handoff rerouting process to improve the performance when the handoff process tack place. The main work and contributions are presented in the following aspects.)

structural problem finite element analysis

利用粒子群优化算法求解100个城市的路径优化问题(Particle swarm optimization algorithm is used to solve the routing problem of 100 cities)

说明：  激光变换中的GS算法,ORA算法，用matlab编程的。(Laser transformation in the GS algorithm, ORA algorithm, using MATLAB programming.)

说明：  使用ELMAN进行负荷预测，并通过仿真的到误差，隐含层数值(Load Forecasting Using ELMAN)

This is simple lmi buliding algorithm with lmi control toolbox.

MATSLISE is a Matlab application for the numerical study of Schringer and Sturm-Liouville problems. MATSLISE allows one to compute the eigen- values and eigenfunctions and to display them graphically. The numerical methods used are the CP methods (abbreviation for Constant reference potential Pertubation Method). (MATSLISE is a Matlab application for the numerical study of Schr鰀inger and Sturm-Liouville problems. MATSLISE allows one to compute the eigen- values and eigenfunctions and to display them graphically. The numerical methods used are the CP methods (abbreviation for Constant reference potential Pertubation Method). )

Particle Methods Compare

Matlab实现灰度图像转换为RGB图像(grayscal—RGB base on Matlab)

说明：  隐马尔科夫模型是关于时序的概率模型，描述由一个隐藏的马尔科夫链随机生成不可观测的状态随机序列，再由各个状态生成一个观测而产生观测序列的过程。隐藏的马尔科夫链随机生成的状态的序列，称为状态序列；每个状态生成一个观测，而由此产生的观测的随机序列，称为观测序列。马尔科夫链由初始概率分布、状态转移概率分布以及观测概率分布确定(The hidden Markov model is a probabilistic model for time series. It describes the process of randomly generating unobservable state random sequences from a hidden Markov chain, and then generating an observation by each state to produce an observation sequence. A sequence of randomly generated states of hidden Markov chains, called a sequence of states; each state produces an observation, and the resulting random sequence of observations is called an observation sequence. Markov chain is determined by initial probability distribution, state transition probability distribution and observation probability distribution)