xiayu

Matlab 官方工具箱使用说明 apiext外部程序使用手册(Matlab toolbox for use in official external program apiext user manual)

说明：  pso生物优化算法应用的实例源代码，根据自己需要可以更改里面的fitness具体情况自己更改 (pso examples of biological optimization application source code, according to their fitness needs to change the situation inside their own change)

图1为均衡带限信号所引起失真的横向或格型自适应均衡器(其中横向FIR系统长M=11), 系统输入是取值为±1的随机序列，其均值为零；参考信号；信道具有脉冲响应： 式中用来控制信道的幅度失真(W = 2～4, 如取W = 2.9,3.1,3.3,3.5等)，且信道受到均值为零、方差(相当于信噪比为30dB)的高斯白噪声的干扰。试比较基于下列几种算法的自适应均衡器在不同信道失真、不同噪声干扰下的收敛情况(对应于每一种情况,在同一坐标下画出其学习曲线): 1）横向/格-梯型结构LMS算法 2）横向/格-梯型结构RLS算法 并分析其结果。(Figure 1 is a band-limited signal caused balanced horizontal or lattice distortion adaptive equalizer (FIR system in which the lateral length M = 11), the system input is the value of a random sequence of ± 1, mean zero reference signal channel has impulse response: Wherein the amplitude of the distortion for the control channel (W = 2 ~ 4, and so as to take W = 2.9,3.1,3.3,3.5), and subjected to a channel with zero mean and variance (equivalent SNR is 30dB) Gaussian white noise. Compare the following types of algorithm-based adaptive equalizer in a different channel distortion, the convergence of different noise interference (corresponding to each situation, draw their learning curve in the same coordinate): 1) landscape/grid- LMS algorithm ladder structure 2) landscape/grid- RLS algorithm ladder structure And analyze the results.)

digital signal processing code

BBO算法的markov分析程序，是利用理论分析算法收敛性的不错代码(The BBO algorithm markov analysis program, theoretical analysis of the convergence of the algorithm good code)

求最大团的算法，Bron-Kerbosch 算法计算图的最大全连通分量(团clique)，matlab实现(Seeking maximum clique algorithm, Bron-Kerbosch algorithm to calculate the maximum fully connected graph component (group clique), matlab achieve)

Provides an implementation of AttributeSet on top of an XmlPullParser.

无速度传感器MARS _PMSG相关仿真资料(MARS _PMSG)

In order to minimize power losses caused by high current and improve the voltage profile in the network distribution, the introduction of dispersed generations also called productions decentralized in distribution network (DG) plays an important role. The installation of DGs directly affects the power requested or purchased. The sizing and placement of DGs in the system must be optimal because a wrong choice has a negative impact on the system behavior. To solve this combinatorial problem, an algorithm known as Firefly algorithm is proposed in this paper. This is a meta-heuristic algorithm inspired by the behavior of fireflies flashing. The main objective of firefly flash is to act as a signaling system to attract other fireflies. Networks tested IEEE69-bus and IEEE33-bus are used to evaluate the effectiveness of this method. The results are compared with those obtained by genetic algorithm (GA) to IEEE69-bus, and Shuffled frog leaping algorithm (SFLA) for IEEE 33-bus.

This is a model of a QPSK modulation system for transmission over a bandpass channel with fc = 100 Hz and B = 30 Hz and AWGN at the receiver. SRRC fi lters with excess bandwidth α = 0.18 are employed. The receiver includes an NDA timing recovery loop followed by a DD phase recovery loop. (This is a model of a QPSK modulation system f or transmission over a bandpass channel with fc = 100 Hz and B = 30 Hz and AWGN at the receiver. SRRC)