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在matlab下的对无线传感器网络中heed算法仿真,相当经典！ (Matlab under in wireless sensor networks heed algorithm simulation, very classic!)

在微电网中综合考虑经济和环境两个目标，以各微源的运行上下限为约束条件，采用粒子群算法在matlab环境下进行了仿真，最后给出了微网各微源的最优机组组合方式和最佳电能交易计划。(The first establish micro multi-objective energy optimal operation model, taking into account both environmental and economic factors in the model, the operation of the various micro-source conditions and micro-grid electric power balance constraints, the establishment of a multi-objective function. To a typical micro-grid running instance As an example, the use of particle swarm optimization strategy and simulation in MATLABLAB environment,micro-source output for microgrid actual environmental economic dispatch .)

说明：  蒲丰投针问题的源程序，运用蒙特卡罗的方法来实现，matlab的源程序(Buffon needle problem of the source, using Monte Carlo methods to achieve, matlab s source code)

利用传输矩阵法计算一维光子晶体能带结构的matlab程序，包含m文件以及计算案例的word文档。(Using the transfer matrix method to calculate one-dimensional photonic crystal band structure of the matlab program, including the case of m-file and to calculate the word document.)

Using logmap algorithm and maxlogmap algorithm with turbo codes.

灰色GM（1,1）预测普通代码和改进之后的代码。(the GM（1,1） predict code)

RNA example T/P Las redes de neuronas artificiales (denominadas habitualmente como RNA o en inglés como: "ANN"1 ) son un paradigma de aprendizaje y procesamiento automático inspirado en la forma en que funciona el sistema nervioso de los animales.

这是一个比较简单的模糊免疫PID的程序，在程序中结合了P模糊与免疫的思想，有较高的实用价值！(This is a relatively simple procedure of fuzzy immune PID in the process of combination of P and the immune fuzzy thinking, a higher value!)

PSOt - a Particle Swarm Optimization Toolbox for use with Matlab. A Particle Swarm Optimization Toolbox (PSOt) for use with the Matlab scientific programming environment has been developed. PSO is introduced briefly and then the use of the toolbox is explained with some examples. A link to downloadable code is provided.

Li near r egr essi on, acti ve learning We arriv ed at the lo gistic regression model when trying to explicitly model the uncertainty about the lab els in a linear c la ss ifier. The same genera l modeling approach p e rmits us to use line ar predictio ns in var ious other co ntexts as well. The simplest of them is regress ion where the go al is to pr e dict a con tin uous resp onse y t ∈ R to e ach example ve ctor. Here to o fo cusing on linear predictions won’t b e inherently limiting as linear predictions can b e easily extended (ne xt lecture).