遗传算法强制交叉算法

clementine8.1中文手册经典的数据挖掘软件说明-clementine8.1 Chinese classic manual data mining software Note

A星算法实现最短路径的选择,使用方便，功能完善，是学习人工智能作业中必须完成的一步。-Choose the shortest path by the method of AStar

支持向量机(SVM)的SMO算法实现。包括matlab源代码文件、支持向量机的pdf文档，源代码word文档。-Support Vector Machine (SVM) algorithm of SMO. Including the matlab source code files, Support Vector Machines pdf documents, source code for word documents.

RBF网络用于函数逼近的一个程序！内部包含几个关于RBF网络的几个算法。-RBF network for function approximation of a process! Internal contains several of the RBF network of several algorithms.

Adaptive Resonance Theory Brain Modeling 人工智能人工神经网络源码-Adaptive Resonance Theory Brain Modeling artificial neural network artificial intelligence source

多层权核k均值算法，原作者Yuqiang Guan，现在将他共享，与大家共同学习！-Multi-layer k-means algorithm for nuclear power, original author Yuqiang Guan, will now share him with everyone learning together!

bp神经网络模拟，是根据以立体的模拟程序-bp neural network simulation, which is based on a three-dimensional simulation program

用BP神经网络逼近函数Z=SIN(X*Y),采用C++编程.-Using BP neural network to approach the function Z = SIN (X* Y), using C++ programming.

matlab编写的BP神经网络算法，的一个应用小程序-matlab prepared by BP neural network algorithm, a small process applications

落煤残存瓦斯量的确定是采掘工作面瓦斯涌出量预测的重要环节,它直接影响着采掘工作面瓦斯涌出量预测的精度,并与煤的变质程度、落煤粒度、原始瓦斯含量、暴露时间等影响因素呈非线性关系。人工神经网络具有表示任意非线性关系和学习的能力,是解决复杂非线性、不确定性和时变性问题的新思想和新方法。基于此,作者提出自适应神经网络的落煤残存瓦斯量预测模型,并结合不同矿井落煤残存瓦斯量的实际测定结果进行验证研究。结果表明,自适应调整权值的变步长BP神经网络模型预测精度高,收敛速度快 该预测模型的应用可为采掘工作面瓦斯涌出量的动态预测提供可靠的基础数据,为采掘工作面落煤残存瓦斯量的确定提出了一种全新的方法和思路。-charged residual coal gas is to determine the volume of mining gas emission rate forecast an important link, which directly affect mining gas emission rate forecast accuracy, and with coal metamorphism, loading coal particle size, the original gas content, exposure time and other factors nonlinear relationship. Artificial neural networks have expressed arbitrary nonlinear relationships and the ability to solve complex nonlinear, time-varying uncertainty and the new ideas and new approaches. Based on this, the author of adaptive neural network loading coal residual gas production forecast model, and a combination of different loading coal mine gas remnants of the actual test results of research