用VISUAL C＋＋编程实现指纹图像的特征提取以及对指纹图像的识别...

BP小波神经网络快速学习算法研究and 小波神经网络程序-BP wavelet neural network learning algorithm for rapid research and wavelet neural network procedures

一个神经网络原型代码，针对的例子为《Perceptron Learning》 (Russell & Norvig， 第742页)-A neural network prototype code, for example for

用VISUAL C＋＋编程实现指纹图像的特征提取以及对指纹图像的识别-Utilising VISUAL C++ to make programs, we can get the characters of image and identify the image of finger mark

专家系统课程设计,用计算机解决SP先生谜题,有详细代码-curriculum design expert system, computer SP Mr. Mystery solved a detailed code

利用VC实现的贝叶斯分类器的源代码，可以用来分类或识别，很值得收藏-The source code of bayes classfication using VC

很好的边缘检测源代码，使用vc++6.0编写，主要用于医学图像的边缘检测-good source edge detection, the use of vc 6.0 preparation, mainly for Medical Image Edge Detection

用于三分类的BP算法程序,matlab源码-three classifications for the BP algorithm procedures, Matlab FOSS

多目标跟踪 (三维)-算法matlab编制-multi-target tracking (3D)- preparation algorithm Matlab

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

JAVA版的蚂蚁算法(Ant Colony Optimization Algorithms) -JAVA version of the ant algorithm (Ant Colony Optimization Algorithms)