空间作战的C++实现的基于神经网络可以学习如何玩好。

MATLAB 下的模式识别特征提取 用于对特征信号的提取-MATLAB under the feature extraction for pattern recognition features of the signal extraction

遗传算法求解优化问题，简明教程加示例- The heredity algorithm solution optimization question, the concise course adds the demonstration

前馈神经网络算法-feed forward neural network algorithm

每条人工鱼迭代20次的位置,每条人工鱼迭代20次的位置,对应的Y的值,每条人工鱼迭代20次的临时位置,公告牌,每条人工鱼迭代20次的位置,对应的Y的临时值，公告牌,人工鱼移动的最大步长,求出最优的函数值；-Each of artificial fish, the location of 20 iterations, each iteration of artificial fish, the location of 20 times the corresponding value of Y, each iteration of artificial fish 20 times a temporary location, bulletin boards, each iteration of artificial fish 20 second position, corresponding to the provisional value of Y, bulletin boards, artificial fish, the biggest step to move, find the optimal function value

是个定时期,不是太有用的东西,-GOOD

模式识别中的寻路算法：A星算法的实现及简单应用-Pattern Recognition routing algorithm: A Star algorithm and a simple application

同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域-comparison with the genetic algorithm, the advantages of PSO is simple and easy to achieve without many parameters need to be adjusted. Now it has been widely used function optimization, neural networks, fuzzy systems control and other genetic algorithm applications

遗传算法求解大学考试时间表问题，没有解压密码的。-Genetic Algorithm for university examination timetable, not extracting passwords.

这是通过A*算法实现8数码问题，求解可能出现的目标状态。通过回溯的方法实现对A*算法。不同于其它状态图的搜索实现8数码问题。-This is the A* algorithm eight digital issues, the solution may target state. Through the method back to the A* algorithm. Unlike other state diagram of the search to achieve eight digital problem.

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