模糊PID控制的一个例子，在Visual C++通过

〈智能优化算法与应用〉介绍各种优化算法：模拟褪火法、遗传算法、禁忌搜索法、神经网络法、混合优化策略等内容。-"intelligent optimization algorithms and application" on the various optimization algorithms : simulated fire faded, genetic algorithm, Tabu search method, neural networks, hybrid optimization strategy content.

本文在学习探讨VSM算法的同时，对SVM在液压系统故障诊断中的应用进行了论述。以液压设备中常见的克令吊为研究对象，运用支持向量机理论对其进行故障诊断的仿真研究和实验研究，其中包括实验系统数学模型的设计、仿真模型的建立(用SIMUL工NK仿真软件建立仿真模型)、特征参数的提取、数据的采集等。-In this paper, VSM learning algorithm to explore the same time, SVM in the hydraulic system fault diagnosis are discussed. With hydraulic equipment in the common crane as the research object, using support vector machine theory of their fault diagnosis simulation and experimental studies, including a mathematical model experimental system design, simulation model (using SIMUL Engineering Simulation NK Software to build simulation models), the extraction of characteristic parameters, data collection and so on.

神经网络的应用程序，里面实现有一定的价值，有较强的参考价值和利用价值-Neural network applications, which achieve a certain degree of value, have a stronger reference to the value and use value

全面讲解了有视觉机器人的解决方案，强烈推荐-comprehensive account of a robot visual solutions and strongly recommended

Genetic Algorithm Optimization Toolbox -Genetic Algorithm Optimization Toolbox

简单遗传算法(SGA)源程序-simple genetic algorithm (SGA) source

最邻近的RBF算法,MATLAB实现,较为有价值的资料.-The nearest RBF algorithm, MATLAB realize, the more valuable information.

模拟退火算法 模拟退火算法(Simulated Annealing,简称SA算法)是模拟加热熔化的金属的退火过程，来寻找全局最优解的有效方法之一。 模拟退火的基本思想和步骤如下： 设S＝{s1,s2,…,sn}为所有可能的状态所构成的集合， f：S―R为非负代价函数，即优化问题抽象如下： 寻找s*∈S，使得f(s*)＝min f(si) 任意si∈S （1）给定一较高初始温度T，随机产生初始状态S （2）按一定方式，对当前状态作随机扰动，产生一个新的状态S’ S’＝S＋sign(η).δ 其中δ为给定的步长， η为[－1,1]的随机数-simulated annealing algorithm (Simulated Annealing, or SA algorithm) is a simulation of heating molten metal in the annealing process, to find the global optimum one of the effective ways. Simulated Annealing basic ideas and the steps are as follows : S = (s1, s2, ..., sn) for all possible state posed by the pool, f : S-R non-negative cost function, that is abstract optimization problems are as follows : Find S* s, making f (s*) = min f (si) arbitrary si S (1) to set a higher initial temperature T, randomly generated initial state S (2) of a certain form, the current state of random disturbance, have a new state S "S" = S+ sign (). delta where given for the step, [-1,1] Random Number

有关摄像头定标的C++代码，标定摄像头的12个参数-Related to camera calibration C++ Code, camera calibration parameters 12

有关bp神经网络预测的-bp of the neural network forecast