单层线性神经网络实例

昨天刚刚看到的A star算法类的实现，不知道对大家有没有用，参考一下吧-Yesterday, I saw a star of the algorithm to achieve category, we do not know of any use it for reference

RBF算法matlab工具箱,里面有详细的说明文档.-RBF algorithm matlab toolbox, which has detailed documentation.

对著名的UCI机器学习库的wine数据集做的朴素贝叶斯分类算法，包含详细源代码和实验报告，不可多得啊-Pairs of well-known UCI machine learning repository data set to do wine Naive Bayesian classification algorithm, with detailed source code and test reports, a rare ah

用遗传算法解决01背包问题，内附测试数据。-Using genetic algorithms to solve knapsack problem 01, enclosing the test data.

几篇关于混合遗传算法方面的好文章。-few mixed genetic algorithm on the excellent article.

神经网络和遗传算法在水科学领域的应用-neural networks and genetic algorithms in the field of water science applications

宽度优先搜索法见cpp1.cpp文件，在vc6.0环境运行实现-width priority search see cpp1.cpp documents in the operation to achieve environmental vc6.0

he algorithm is equivalent to Infomax by Bell and Sejnowski 1995 [1] using a maximum likelihood formulation. No noise is assumed and the number of observations must equal the number of sources. The BFGS method [2] is used for optimization. The number of independent components are calculated using Bayes Information Criterion [3] (BIC), with PCA for dimension reduction.

A算法，应用于人工智能-algorithm, used in artificial intelligence

K-均值聚类算法的编程实现。包括逐点聚类和批处理聚类。K－均值聚类的的时间复杂度是n*k*m，其中n为样本数，k为类别数，m为样本维数。这个时间复杂度是相当客观的。因为如果用每秒10亿次的计算机对50个样本采用穷举法分两类，寻找最优，列举一遍约66.7天，分成3类，则要约3500万年。针对算法局部最优的缺点，本人正在编制模拟退火程序进行改进。希望及早奉给大家，倾听高手教诲。-K-means clustering algorithm programming. Point by point, including clustering and clustering batch. K-means clustering of the time complexity of n* k* m, n samples, several types of k, m sample dimension. The time complexity is a very objective. Because if we use one billion times per second the computer using 50 samples of two exhaustive method to find the optimal set out again about 66.7 days, divided into three categories, offering 3,500 years. Local optimal algorithm against the shortcomings, I was prepared simulated annealing process improvements. Early Feng hope for everyone, listen to the master teachings.