GPS_INS位置组合仿真-Matlab源代码

吉布斯抽样问题的MATLAB工具箱吉布斯抽样问题的MATLAB工具(gibss toolbox for matlab)

Noise Remove Code in Matlab

这是一段遗传算法训练RBF网络的matlab程序，大家可以下载参考。(This is a genetic algorithm for training RBF network program, you can download the reference.)

Eigenvalues and their algebraic multiplicity.

说明：  克隆选择的免疫算法，结合克隆选择原理，提出克隆算子(Immune clonal selection algorithm, combined with clonal selection theory, proposed by cloning Operator)

DOA经典算法系列之MUSIC MUSIC算法的深入研究改进算法众多,这里提供Music系列一:信号子空间法(DOA series of classical algorithm MUSIC MUSIC- depth study of the many improved algorithm, Music here for a series : signal-space law)

说明：  忽略损耗的电网经济调度，三个发电机组，可以计算出力和成本。(In the economic dispatch of power network with neglect of loss, three generators can calculate the power and cost.)

说明：  运用迭代法计算信道容量，MATLAB语言，输入参数：先验概率矩阵，输出：信道容量。(The use of iterative method to calculate the channel capacity, MATLAB language, input parameters: a priori probability matrix, the output: channel capacity.)

这是一个最基本的OFDM系统仿真，说明一个信号通过OFDM系统所产生信号对比(This is a basic OFDM system simulation, that a signal generated by the signal OFDM system comparison)

In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discretedata linear filtering problem [Kalman60]. Since that time, due in large part to advances in digital computing, the Kalman filter has been the subject of extensive research and application, particularly in the area of autonomous or assisted navigation. A very “friendly” introduction to the general idea of the Kalman filter can be found in Chapter 1 of [Maybeck79], while a more complete introductory discussion can be found in [Sorenson70], which also contains some interesting historical narrative.(In 1960, R. E. Kalman published his famous paper describ ing a recursive solution to the discretedata li near filtering problem [Kalman60]. Since that time, due in large part to advances in digital computi Vi, the Kalman filter has been the subject of extens ive research and application. particularly in the area of autonomous or assis ted navigation. A very "friendly" introductio n to the general idea of the Kalman filter can be f ound in Chapter 1 of [Maybeck79] while a more complete introductory discussion can be found in [Sorenson70] which also contains some interesting historic al narrative.)