MATLAB-array-antenna-pattern

matlab从入门到精通第9章书后源码对初学者很有帮助(matlab)

曲面拟合的程序，网上很多地方有，但是都是错的，我改正后上传了(Surface fitting procedure, there are many places online, but it is wrong, I uploaded the corrected)

利用matlab软件来绘制Chessboard，可以用于摄像机张正友方法的标定(Using matlab software to draw the Chessboard, the camera can be used to calibrate the method of Zhang Zhengyou)

三相风力发电控制系统。已经建好的模型，pwm控制(Three-phase wind power control systems. The model has been built)

1. this function is for image (width=176, hight=144) only. but you can modify the related value to fit yours. 2. it is used to evaluate the motion vector between the Nth image and N-1th image. 3. it use Three Steps search algorithm. 4. this algorithm finds the closest pixel near the original pixel in three times.(1. This function is for image (width = 176, hight = 144) only. But you can modify the related value to fit yours.2. It is used to evaluate the motion vector between the Nth image and N-1th image.3 . it use Three Steps search algorithm.4. this algorithm finds the closest pixel near the original pixel in three times.)

完成相关向量机的回归与分类的功能，采用经典例子(The relevance vector machine regression and classification)

《MATLAB神经网络30个案例分析》中的第11个例子，案例11 连续Hopfield神经网络的优化-旅行商问题优化计算。希望对大家有一定的帮助！(The MATLAB neural network analysis of 30 cases of 11 examples, case 11 continuous Hopfield neural network optimization- traveling salesman problem optimization calculation. Hope to have certain help to everybody! )

很精准的潮流计算，适合100节点内的的潮流计算(power flow)

ofdm system ofdm systemofdm systemofdm systemofdm systemofdm systemofdm systemofdm systemofdm system

一个身体部位比较模型的新方法网络（BAN）的，可以容纳多个环节和多个科目。所述的绝对测量允许跨频谱可能刻画的比较 从单参数为整个合奏，通过基于参数化到每个活动，每个学科和每个环节模型。使用错误，并明确之间权衡复杂性，在一个善良的适应措施相结合，显示有重要的影响时，适用于一系列典型的禁止通道数据。它是有不同的 在模式的选择的影响，以及它相关的复杂性，混合活动的“日常”的数据，设置活动相比，动态数据（例如步行）。平均路径损耗的不足，甚至位数的路径损失的措施，作为唯一的表征还强调“禁止通道。 (A new approach to compare models for body area networks (BAN) that accommodates multiple links and multiple subjects is presented. The absolute measure described allows comparison across a spectrum of possible characterizations ranging from single-parameter for an entire ensemble, through to per-activity, per-subject and per-link based parameterized models. The use of an explicit trade-off between error and complexity, combined in a goodness-of-fit measure, is shown to have important consequences when applied to a range of typical BAN channel data. It is shown that there are different implications in choice of model, and it’s associated complexity, for mixed-activity “everyday” data, when compared with set-activity dynamic data (e.g. walking). The deficiency of mean path loss, or even median path loss measures, as a sole characterization of the BAN channel is also highlighted.)