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帮您制定最佳的决策，欢迎您的访问，谢谢下载(Help you make the best decisions, welcome your visit, thank you download)

求解三体问题，并画动态图像。同时含有解释三体问题的pdf文件(Solving the three-body problem, and draw a dynamic image. Also contains a pdf file explaining the three-body problem)

matlab code for SEP protocol

希望对大家有所帮助，祝大家学习进步，有时间共同探讨问题 (Would like to help everyone, I wish everyone to learn and progress, have time to explore the issue of)

扩展卡尔曼滤波程序，matlab 仿真。(EKF simulation)

ball & beam lead PD matlab m-file

说明：  搭建BPSK+编码+交织仿真通信链路，仿真分析在不同信道条件下的不同译 码算法的误比特率性能。基于matlab，搭建BPSK+卷积编码+交织通信收发链路， 仿真参数如下： 1) 信源比特速率： 100 b R kbps = 2) 卷积编码：码率为1/2，生成多项式为（561,753） 3) 译码方式：维特比译码（硬判决译码、软判决译码（8 比特量化）） 4) 交织：行列交织，交织器深度为100bit，宽度为10； 5) 仿真点数：106(BPSK+ coding + interleaved simulation communication link)

matlab神经网路工具箱详细教程，通过matlab实现多种网络(Matlab neural network toolbox detailed tutorial)

在机动目标跟踪中，机动目标模型是机动目标跟踪的基本要素之一，一般希望机动目标模型能准确表征目标机动时的各种运动状态。比较常用的模型有匀速运动(CV)模型、匀加速运动(CA) 模型、时间相关模型（Singer）和机动目标“当前”统计模型。上述模型均采用机动频率表征目标的机动情况。在应用当中，通常采用固定的机动频率，这就表示机动目标的机动时间是一定的，而实际上机动目标的机动时间是不断变化的，也就是说机动频率是不断变化的，采用固定机动频率必然会带来误差。采样周期在0.5—2S时，机动频率越小跟踪精度越高[1]，但机动频率仍然是固定值。本文提出的基于神经网络的机动频率自适应调整方法可以使机动频率随机动而变化，从而提高状态估计的准确性，提高跟踪精度。本文将小波神经网络用于机动目标跟踪中机动频率的自适应调整，该算法对机动目标“当前”统计模型中的机动频率进行实时修改, 从而自适应的改变机动频率,使跟踪算法与目标的真实状态更接近。该算法采用小波神经网络的离线训练，实时性好。(The maneuver of the maneuvering target is uncertain. The maneuvering frequency is constantly changeable, but traditionally it is beforehand determined as a constant based on the target state estimation in the state model of the maneuvering target. The maneuver of the maneuvering target makes the kinematics equation of the target model mismatch with the practical motion model and the tracking error will be increased. Based on the advantages of the self-learning, the rapid convergence rate and the nonlinear approximation ability of the wavelet neural network, it was put forward to be used in the field of target tracking in the paper. The new residual is used as the input of the wavelet neural network, the output of the network is used to adjust adaptively the maneuvering frequency of the CS model. The algorithm is more close to the real state of the target. The simulation results showed that tracking error can be reduced and the tracking accuracy can be improved.)

Optical Charactor Recognition