dynamicsimulink

OFDM system in matlab Simulink with Ultra Wide Band

matlab编的6节点电网规划扩展改进粒子群算法程序，相当难得的优秀程序(matlab code of the 6-bus network planning expansion of improved particle swarm optimization process, a rare quality procedures)

L4_1.m: SVD轉換(程式) L4_2.m: 影像轉換的能量集中能力(程式) L4_3a.m: 小波轉換之多重解析架構(程式) L4_3b.m: 小波轉換係數與影像的方向性(程式) energy.m: 能量集中(函式) L4_1.bmp: 影像檔 L4_2.bmp: 影像檔 L4_3b.bmp: 影像檔 woman.mat: Matlab的矩陣變數檔 (L4_1.m: SVD conversion (program) L4_2.m: image conversion of energy to focus capacity (program) L4_3a.m: the multi-resolution wavelet transform architecture (program) L4_3b.m: wavelet transform coefficient and the direction of the image (the program) energy.m: energy concentration (function) L4_1.bmp: image file L4_2.bmp: image file L4_3b.bmp: image file woman.mat: Matlab matrix variable file)

In this simulink model , 16-QAM modulation and demodulation is simulated .A square root raised cosine pulse shaped signal is modulated and given as input to the demodulator.The received signal is demodulated and given to the detector where it is matched filtered and down-sampled .The effects of symbol timing offset can be seen by varying the phase of down-sampling operation.The signal transmitted is recovered at the detector output.

SVM的VC程序，有matlab接口，便于调用，还有例子，用于模式识别，分类，预测。(SVM, VC procedures, matlab interface, easy call, as well as an example, for pattern recognition, classification and prediction)

hybrid Archive-based Micro Genetic Algorithm (AMGA) on a set of bound-constrained synthetic test problems is reported. The hybrid AMGA proposed in this paper is a combination of a classical gradient based single-objective optimization algorithm and an evolutionary multi-objective optimization algorithm.

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.

学习期间跟老师做的一个project,simulink 在工业控制中（石油开采）的基本应用。(Studying with a teacher to do a project, simulink in industry control (oil industry) of the basic application.)

说明：  双馈电机数学模型，基于simulink，如果需要可以提供S函数的模型(Doubly-fed machine mathematical model, based on simulink, if necessary can provide a model S function)

说明：  提出了一种新的基于离散小波变换(DWT)与奇异值分解(SVD)相结合的数字图像水印算法。该算法将原始图像作小波分解并将小波分解得到的低频子带进行分块，对每一块进行奇异值分解后，选取每块中最大的奇异值通过量化的方法嵌入经过Arnold置乱后水印信息。(A new wavelet transform based on discrete (DWT) and Singular Value Decomposition (SVD) combination of digital image watermarking algorithm. The algorithm for wavelet decomposition of the original image and wavelet decomposition low-frequency sub-band are divided into blocks, each a singular value decomposition, the selection of each block by the largest singular value methods to quantify Arnold scrambled after embedding the watermark .)