资源列表
[matlab例程] FindingLocErrorWSN
说明:localization error for wireless sensor network by Matlab<ruobaa> 在 2025-09-27 上传 | 大小:4kb | 下载:0
[matlab例程] protocol_HEED
说明:this i sHeed Protocol for wireless sensor network<ruobaa> 在 2025-09-27 上传 | 大小:196kb | 下载:0
[matlab例程] multiple_DOA_estimate
说明:采用10阵元的阵元间距为1/2波长的均匀线阵,估计2个不相干的信号源的波打方向。其中样本数为100,S/N分别为10dB,20dB。信号源来自-10°和40°。程序里面包含用MUSIC,RootMUSIC,ESPRIT,MVDR,F-SAPES算法实现的DOA估计-A 10-element array element spacing of 1/2 wavelength of the uniform linear array, it is estimated that two unrelated<song> 在 2025-09-27 上传 | 大小:44kb | 下载:0
[matlab例程] kalman_filter
说明:用卡尔曼滤波算法估计一个四阶线性预测模型的最优权值,并附有仿真图形-Kalman filter algorithm to estimate the optimum weights of a fourth-order linear prediction model with simulation graphics<song> 在 2025-09-27 上传 | 大小:31kb | 下载:0
[matlab例程] RLS
说明:本程序基于一阶AR模型,u(n)=-0.99u(n-1)+v(n)的线性预测。白噪声v(n)方差0.995.FIR滤波器的抽头数为2.遗忘因子0.98.用RLS算法实现u(n)的线性预测。并附有仿真图片-This procedure is based on a first-order AR model, u (n) =-0.99u (n-1)+v (n) of the linear prediction. White noise v (n) the number of taps of the t<song> 在 2025-09-27 上传 | 大小:42kb | 下载:0
[matlab例程] LMS
说明:基于一阶AR模型u(n)=0.99u(n-1)+v(n),白噪声方差0.93627.步长0.05.分别使用M=2和M=3抽头的滤波器,用LMS算法实现u(n)的线性预测估计。并附仿真图已被参考。-Based on a first-order AR model u (n) = 0.99u (n-1) the+v (n), the white noise variance 0.93627 step 0.05. Respectively with M = 2 and M = 3-tap filter,<song> 在 2025-09-27 上传 | 大小:32kb | 下载:0
[matlab例程] FMINSEARCHBND
说明:Fminsearch does not admit bound constraints. However simple transformation methods exist to convert a bound constrained problem into an unconstrained problem.<Karthi> 在 2025-09-27 上传 | 大小:20kb | 下载:0
[matlab例程] chess2011_01_20
说明:Play chess against "Greedy Edi".<Karthi> 在 2025-09-27 上传 | 大小:553kb | 下载:0
[matlab例程] mpcsimulink
说明:a Simulink block to use the state space MPC code is developed. Two examples to work with continous-time state space plant model and discrete-time state space plant model are included.<Karthi> 在 2025-09-27 上传 | 大小:54kb | 下载:0
[matlab例程] Pade
说明:The Padé approximant often gives better approximation of the function than truncating its Taylor series, and it may still work where the Taylor series does not converge. For these reasons Padé approximants are used extensively in computer calculation<Karthi> 在 2025-09-27 上传 | 大小:17kb | 下载:0