matlab中求解最大最小问题的蚂蚁算法源代码。很有参考价值。...

《游戏编程中的人工智能技术》一书中123章源代码- Game programming in artificial intelligence technology, one source book, Chapter 123

java神经网络工具箱-neural network toolbox

由 Visual C++ 编译过，可以求解任何个数城市的tsp问题，只要输入城市坐标即可。-By Visual C++ Compiled, you can to solve any number of cities tsp, so long as you enter the city coordinates.

用的是倒谱,加神经网络~不过没有成功,稳定性很差-using the cepstrum, neural network to increase but not a success, poor stability

Parzen 窗 和 K近邻法进行概率密度估计 还带一个示波器控件.-Parzen window and K-nearest neighbor method probability density is estimated to bring an oscilloscope control.

用matlab语言编写的,该程序名为模拟退火算法m 源程序-Matlab language used, the procedure called simulated annealing algorithm m source

简单一元函数优化实例，利用遗传算法计算下面函数的最大值-One dollar a simple example of function optimization using genetic algorithm function below the maximum

模拟退火算法求解经典图论中的图着色问题的源程序-simulated annealing algorithm classic graph coloring map of the source

　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

自适应步长BP神经网络训练算法，采用最小误差和梯度下降法更新权值- BP neural network training anaysis， realized by using error feed back, gradient descent applied updating of synaptic weights