Uncertainty-Analysis-flood-damage

matlab lecture note 2. by KAIST.

无损卡尔曼滤波源程序，可读性强，适用范围广，可在此基础上做进一步改进(Kalman filter lossless source, readable, wide application, can be done on the basis of further improvements)

说明：  5位0-7数字的rs编码，解码和纠错，两位纠错码(Rs numbers 0-7 5 encoding, decoding, and error correction, the two error correction code)

现代通信系统（实用MATLAB)第六章程序(Modern communication systems (useful MATLAB) Chapter VI program)

通信原理相关实验程序及报告，包括AM,DSB-SC、SSB信号等的波形及频谱。(Communication Theory experimental procedures and reports, including AM, DSB-SC, SSB signal waveform and spectrum.)

空时自适应处理（全自由度），包括杂波谱图仿真，最优处理器仿真图等(Space-time adaptive processing (all degrees of freedom), including the clutter spectrum simulation, optimal processor simulation diagrams)

matlab环境下编写的fft算法实现的源代码(prepared environment matlab algorithm fft source code)

用于采用HHT方法处理地震波等信号，生成固有模式函数EMD的Matlab源程序(the EMD program of HHT method in analyzing wave)

Q-learning (Watkins, 1989) is a simple way for agents to learn how to act optimally in controlled Markovian domains. It amounts to an incremental method for dynamic programming which imposes limited computational demands. It works by successively improving its evaluations of the quality of particular actions at particular states. This paper presents and proves in detail a convergence theorem for Q,-learning based on that outlined in Watkins (1989). We show that Q-learning converges to the optimum action-values with probability 1 so long as all actions are repeatedly sampled in all states and the action-values are represented discretely. We also sketch extensions to the cases of non-discounted, but absorbing, Markov environments, and where many Q values can be changed each iteration, rather than just one.

用于圆柱绕流时候流畅区域的网格划分matlab程序(When the grid is used to smooth the flow around a cylinder divided matlab program area)