Adaptive Resonance Theory Brain Modeling 人工智能人工神经网络源码

关于鱼群算法的一个程序，希望对学习智能算法的朋友有所帮助。-Fish-swarm algorithm on a procedure, and they hope to learn from intelligent algorithm friend help.

这是一个非常好用的蚂蚁算法(蚁群算法)模拟程序,很好用噢,快来试试吧-This a very user-friendly ant algorithm (ant colony algorithm) simulation program, easy to use Oh, Come and try!

采用MFC技术，实现蚁群算法和遗传算法，形象化了算法实现的过程，有助于我们的学习和理解。-Use of MFC technology to realize ant colony algorithm and genetic algorithm, visualization of the algorithm implementation process, contribute to our learning and understanding.

vc++ 实现的人工神经网络bp网络的源代码-bp Algorithm

这是目前网上所能找到的最全面的adaboost的C++算法，但是需要自己训练的数据-This is the Internet can find the most comprehensive adaboost C algorithms, However, it needs its own training data

经典算法clique的java实现-Classical algorithm clique of the java implementation! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

TSP双种群蚁群算法。还包括了“30城市TSP问题数据与最优解.mat”，“75城市TSP问题数据.mat”，和“442TSP问题数据与算法对比.mat”-TSP-Stocks ant colony algorithm. Also include "30 cities TSP data and the optimal solution. Mat, " "75 cities TSP data. mat, " and "442TSP problem with the algorithm for data comparison. mat "

用最小二乘法计算分形图案的维数。试试看怎么样。请多包涵！！ -least squares method used fractal pattern dimension. Try how kind. I feel apologetic for this! !

手写汉字识别 应用SVM和神经网络进行识别,可自行训练-Handwritten Chinese Character Recognition Application of SVM and neural networks to identify their own training

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