CCA_SNR10

跳频系统的matlab仿真，基本的M序列生成源代码()

一个小的matlab程序利用RBFNN进行函数拟合(A small matlab program using RBFNN for function fitting)

针对分布式驱动电动汽车的质心侧偏角估计编写了UKF估计代码(A UKF estimation code is developed for the estimation of the center-of-mass slip angle of the distributed drive electric vehicle)

基于RBF神经网络辨识的单神经元PID模型参考自适应控制基于RBF神经网络辨识的单神经元PID模型参考自适应控制(Based on RBF Neural Network Identification of single neuron PID model reference adaptive control based on RBF Neural Network Identification of single neuron PID model reference adaptive control)

无线传感器网络定位用到的无线传输模型 Regular Model, Logarithmic Attenuation Model, DOI Model, RIM Model (Wireless sensor network localization using wireless transmission model, Regular Model, Logarithmic Attenuation Model, DOI Model, RIM Model Achieve functional distance and transmission attenuation swap ~~~~~~~~~~~~~~~~~~~~~~Regular Model~~~~~~~~~~~~~~~ In this model, the radio range is a circle ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ReceivedSignalStrength=SendingPower-PathLoss e.g, RSS=Pt-Pl(d0)-10*η*log10(dist/d0) Pt:transmit power pl(d0):the pass loss for a reference distance of d0 ηis the path loss exponent above parameters are saved in ../Parameters_Of_Models.mat dist:distance between sender and receiver(m) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ typical value:Pt=0-4dBm(max),Pl(d0)=55dB(d0=1m),η(2~4)=4(indoor,outdoor) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~Logarithmic Attenuation Model~~~~~~~~~ In this model, the radio range is a circle ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ReceivedSignal)

Finds the convolution matrix of two input vectors

说明：  用7次多项式进行机器人关节空间的轨迹规划(The trajectory planning of robot joint space is carried out by using 7 degree polynomial)

说明：  小波分解 语音降噪 例子 正常运行 matlab(Wavelet decomposition speech denoising example normal operation)

数学建模与Matlab参考书——《数学建模与数学实验》——赵静，对数学建模很有帮助(Mathematical Modeling and Matlab reference book- "Mathematical modeling and mathematical experiment" by Zhao Jing,it s very useful in mathematical modeling.)

摘　要:提出一种新的基于Pareto多目标进化免疫算法(PMEIA)。算法在每一代进化群体中选取最优非支配抗体保存到记忆细胞文档中 同时引入Parzen窗估计法计算记忆细胞的熵值,根据熵值对记忆细胞文档进行动更新,使算法向着理想Pareto最优边界搜索。此外,算法基于点在目标空间分况进行克隆选择,有利于得到分布较广的Pareto最优边界,且加快了收敛速度。与已有算法相比, PMEIA在收敛性、多样性,以及解的分布性方面都得到很好的提高。(Abstract:This paperproposed a new pareto-based multi-object evolutionary mi mune algorithm(PMEIA). PMEIA selected optmi alnon-dominated antibodieswhichwere then reserved inmemory cellarchive, and introducedParzenwindow to calculate entropy ofmemory cells. Updated thememory cell archive according to entropy ofmemory cells. This guarantees the conver- gence to the true Pareto fron.t Moreover, the performance of clone selection was dependent on distribution in the objective space, whichwas favorable forgetting awidely spreadPareto frontand mi proving convergence speed. Comparedwith the exis- ted algorithms, the obtained solutions ofPMEIA havemuch betterperformance in the convergence, diversity and distribution.)