神经网络中的感知机的学习算法的c语言程序源代码

DBSCAN是一种性能优越的基于密度的空间聚类算法．利用基于密度的聚类概念，用户只需输入一个参数，DBSCAN算法就能够发现任意形状的类，并可以有效地处理噪声．-DBSCAN is a superior performance of space-based density clustering algorithm. The use of the concept of density-based clustering, the user can enter a parameter, DBSCAN algorithm to be able to find any type of shape, and can effectively deal with noise.

SOBI算法，采用二阶统计量进行盲分离。-SOBI algorithm, using second-order statistic for blind separation.

神经网络计算双色球程序，采用快速的神经网络函数来实现，准确度高-Loans neural network computing procedures, the use of a fast neural network function to realize high accuracy

遗传算法基本原理PPT下载，适合初学者学习，一共有四部分。-The basic principles of genetic algorithms PPT download, suitable for beginners to learn, a total of four parts.

是个定时期,不是太有用的东西,-GOOD

基于隐马尔可夫模型的人脸识别源代码,是作模式识别的最好学习代码-Hidden Markov Model-based face recognition source code for pattern recognition is the best learning code

自适应遗传算法，采用C编写，比较适应开始学习遗传算法的人学习-Adaptive genetic algorithm, using C write, compare the genetic algorithm to adapt to start learning to learn

用粒子群方法解决TSP（旅行商）问题，按照TSP1.m中的格式输入程序参数即可-Using Particle Swarm solution to TSP (traveling salesman) problems, in accordance with the format in TSP1.m parameter input program

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