lms

grid_world example with reinforcement learning

EM 算法是求参数极大似然估计的一种方法，它可以从非完整数据集中对参数进行估计，是一种非常简单实用的学习算法。这种方法可以广泛地应用于处理缺损数据、截尾数据以及带有噪声等所谓的不完全数据，可以具体来说，我们可以利用EM算法来填充样本中的缺失数据、发现隐藏变量的值、估计HMM中的参数、估计有限混合分布中的参数以及可以进行无监督聚类等等。(Expectation Maximization image segmentation Input: ima: grey color image k: Number of classes Output: mask: clasification image mask mu: vector of class means v: vector of class variances p: vector of class proportions )

SBOGA 用顺序选择遗传算法求解一维无约束优化问题 (SBOGA order to select genetic algorithm for solving one-dimensional unconstrained optimization problems)

说明：  binary particle swarm optimization for features selection

code to get optical flow using sum of robust differences method

载波频率不同的多个DSB信号混合后，在不知道源信号的情况下，将混合后的DSB信号分离出来(A different carrier frequency signals of multiple DSB after mixing the source signals without knowing the circumstances, would be a mixture of the DSB signal separation)

Note making using OCR

本程序，包含QPSK，OQPSK的产生过程，I、Q领路信号抽取，他们的频谱，包络情况，经过带通滤波器后的变化，经过非线性放大器后的频谱变化等。是一个很好的学习例程！(This procedure, including QPSK, OQPSK production process, I, Q signal extraction lead the way, their spectrum envelope case, after band-pass filter after the change, through non-linear changes in the spectrum after the amplifier. Is a good learning routines!)

WCDMA FDD Simulink Models

基于模型设计的介绍,给大家分享一下，希望刘杰博士不要见怪。(model based design)