CPN算法的matlab实现，CPN人工神经网络

神经网络仿真的基本程序- Nerve network simulation main routine

匈牙利命名法则，关于在编程中变量的命名的一个很好的参考！-Hungary naming rules，a good reference of naming variables in the programming！

模糊控制逻辑飞机着陆问题算法的vc编程实现-Fuzzy logic control of the aircraft landing problem vc Programming Algorithm

模拟退火算法用于求解旅行商问题的matlab源程序-simulated annealing algorithm for solving the traveling salesman problem Matlab source

模糊pid仿真，先运行模糊推理系统设计程序a.m，并调入内存，然后运行模糊控制程序b.m,对pid控制参数进行整定。-Pid fuzzy simulation, Fuzzy Inference System to run the design process am, and transferred to memory, and then run the procedure of fuzzy control bm, of pid control tuning parameters.

介绍模拟退火算法的经典资料，最后附了一些经典的例子-Describes the classic simulated annealing algorithm information, finally attached to a number of classic examples of

　人工神经网络(Aartificial Neural Network，下简称ANN)是模拟生物神经元的结构而提出的一种信息处理方法。早在1943年，已由心理学家Warren S.Mcculloch和数学家Walth H.Pitts提出神经元数学模型，后被冷落了一段时间，80年代又迅猛兴起［1］。ANN之所以受到人们的普遍关注，是由于它具有本质的非线形特征、并行处理能力、强鲁棒性以及自组织自学习的能力。其中研究得最为成熟的是误差的反传模型算法(BP算法，Back Propagation)，它的网络结构及算法直观、简单，在工业领域中应用较多。-Artificial Neural Networks (Aartificial Neural Network, under the short title of ANN) is a simulation of the structure of biological neurons and the information put forward a method of treatment. At early 1943, by Warren S. Mcculloch psychologists and mathematicians Walth H. Pitts proposed mathematical model of neurons, was left out in the cold for some time, and the rapid rise of the 80" s [1]. ANN because the general concern of the people is the essence of it is because of the non-linear characteristics of parallel processing capabilities, strong robustness and self-organizing capacity of self-learning. Research one of the most sophisticated are the error-propagation model of the algorithm (BP algorithm, Back Pro

神经网络预测程序代码 -Neural network code

使用遗传算法训练的神经网络VC++源代码，经测试，该算法有效。- The use heredity algorithm training nerve network VC source code, after the test, this algorithm is effective.

this demo is to show you how to implement a generic SIR (a.k.a. particle, bootstrap, Monte Carlo) filter to estimate the hidden states of a nonlinear, non-Gaussian state space model.-this demo is to show you how to implement a ge neric SIR (a.k.a. particle, the bootstrap. Monte Carlo) filter to estimate the hidden stat es of a nonlinear. non-Gaussian state space model.