本程序代码是人工智能中九宫搜索的解决 采用Java语言

〈智能优化算法与应用〉介绍各种优化算法：模拟褪火法、遗传算法、禁忌搜索法、神经网络法、混合优化策略等内容。-"intelligent optimization algorithms and application" on the various optimization algorithms : simulated fire faded, genetic algorithm, Tabu search method, neural networks, hybrid optimization strategy content.

nuralnetwork using back propegation and genatic algorithm implementation

改进的量子搜索算法的论文，用于模式识别等方面-Quantum search algorithm to improve the paper for pattern recognition, etc.

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

an approximate SVM solver, useful for very large dataset.

本人编写的incremental 随机神经元网络算法，该算法最大的特点是可以保证approximation特性，而且速度快效果不错，可以作为学术上的比较和分析。目前只适合benchmark的regression问题。 具体效果可参考 G.-B. Huang, L. Chen and C.-K. Siew, “Universal Approximation Using Incremental Constructive Feedforward Networks with Random Hidden Nodes”, IEEE Transactions on Neural Networks, vol. 17, no. 4, pp. 879-892, 2006. -I prepared by incremental random neural network algorithm, which is characterized by the largest approximation properties can be guaranteed, and fast good results can be used as an academic comparison and analysis. The current benchmark is only suitable for the regression problem. Specific effects may refer G.-B. Huang, L. Chen and C.-K. Siew,

本疾病诊断专家系统(免费软件)只限于个人研究学习使用，任何单位和个人不得将此软件用于任何商业用途。-the disease diagnostic expert system (free software) is limited to their own research to learn how to use, and no unit or individual is allowed this software for any commercial purposes.

好不容易找到的外国博士论文,关于Text-independent speaker recognition-Hard to find foreign doctoral thesis on the Text-independent speaker recognition

The tca package is a Matlab program that implements the tree-dependent component analysis (TCA) algorithms that extends the independent component analysis (ICA), where instead of looking for a linear transform that makes the data components independent, we are looking for components that can be best fitted in a tree structured graphical model. The TCA model can be applied in any situation where the data can be assumed to have been transformed by an unknown linear transformation.