一个自主机器人的电力传输线检查可引起电压的输电线路作为电源来源提出。该机器人采用电流互感器的主要促使目前从输电线路和远见功能浏览。结果表明，该机器人可以 自...

支撑向量机算法的一种改进算法，是基于增量学习的算法-Support Vector Machine Algorithm an improved algorithm is based on the incremental learning algorithm

用于multivariate时间序列分类，利用了weka机器学习系统（本站可下载）-for multivariate time series classification, the use of machine learning system weka (site available for download)

传统遗传算法程序。开发环境为visual C-Traditional genetic algorithm procedure. Development environment for visual C

用VC++实现的BP（反向传播负反馈）神经网络算法-with VC BP (BP negative feedback) neural network algorithm

利用小波函数做神经网络的激活函数 这是一个简单的方法在使用BP网络的时候 可以直接调用小波函数 -Does the use of wavelet function neural network activation function is a simple method in the use of BP networks can directly call the wavelet function

用c语言实现人工智能神经元网络的adaline算法-C language used in artificial intelligence neural network algorithm adaline

这是反向传播算法的C++程序，对于研究BPNN的人来说，具有很好的参考价值-This is the back-propagation algorithm C program for studying the BPNN people, have a good reference value

本人做的遗传算法源代码，在FORTRAN下打开即可以，可在WINDOUS环境下运行-I do genetic algorithm source code, under the FORTRAN that can be opened, in WINDOUS operating environment

关联规则并行算法Count Distribution的C与MPI实现-Parallel Algorithm for Association Rules Count Distribution of C and MPI realize

GENNET是一个通过遗传算法优化权重的16个神经元的全连接神经网络。训练目的是使网络在一定假设的激励下，随时间产生同目标函数同样的响应，即:可以把此网络制作为一个任意波形的信号发生器。 程序分3个示例说明遗传算法进化过程中网络输出的变化趋势。可以看出网络输出（蓝色曲线）随着时间变化逐渐逼近目标函数（红色曲线）。此程序不仅可以学习遗传算法，也可以用于研究全连接网络的直观示例。-GENNET is a genetic algorithm to optimize the weights by the 16 neurons fully connected neural networks. Training purposes is to enable network inspired by certain assumptions, over time, produce the same response with the objective function, namely: the network can be produced as an arbitrary waveform signal generator. Procedure, three examples illustrate the evolution of genetic algorithms in the network output trends. We can see that the network output (blue curve) changes over time, gradually approaching the objective function (red curve). This program can not only learn from the genetic algorithm can also be used to study visual example of a fully connected network.