a389cac43685

matlab gives us good platform for statistics regarding problem

无刷双馈电机S函数模型，是状态方程，编写的，可以直接使用的。(The BDFM S-function model, equation of state, written, and can be used directly.)

粒子群优化算法求解车辆路径问题，对初学者来说，是很有帮助的。(Particle Swarm Optimization for vehicle routing problem, for beginners, it is very helpful.)

这是一个用Matlab编写的用于WSN中质心算法计算节点位置的源程序(this resource is used to calculate the locationof the nodes in WSN)

LTE FDD数字蜂窝移动通信网 Uu接口技术要求 第1部分：物理层概述 (TD-LTE digital cellular mobile telecommunication network Uu Interface Technical Requirement – Part 1 : Physic Layer General)

kalman滤波的基本原理，在飞行器轨迹预测方面的应用，非常有用(The basic principle of the kalman filter in aircraft trajectory prediction application, is very useful)

This paper presents an overview of our most recent results concerning the Particle Swarm Optimization (PSO) method. Techniques for the alleviation of local minima, and for detecting multiple minimizers are described. Moreover, results on the ability of the PSO in tackling Multiobjective, Minimax, Integer Programming and
1 errors-in-variables problems, as well as problems in noisy and continuously changing environments, are reported. Finally, a Composite PSO, in which the heuristic parameters of PSO are controlled by a Differential Evolution algorithm during the optimization, is described, and results for many well-known and widely used test functions are given.

this is matlab code

对原始灰度图像进行图像压缩，对此图进行 8×8 DCT 变换后，分别只保留 DC系数，以及保留前 4、8、16 个变换系数(DC＋AC) ，然后再分别进行 IDCT 变换 (The original gray image compression, this map for 8 × 8 DCT transform, respectively, to retain only the DC coefficient, and to retain the former 4,8,16 months transform coefficients (DC+ AC), and then separately IDCT transform)

在网络异常检测中,为了提高对异常状态的检测率，降低对正常状态的误判率,本文提出一种基于量子粒子群优化算法训练小波神经网络进行网络异常检测的新方法。利用量子粒子群优化算法(QPSO)训练小波神经网络,将小波神经网络(WNN)中的参数组合作为优化算法中的一个粒子,在全局空间中搜索具有最优适应值的参数向量。( In order to improve the detection rate for anomaly state and reduce the false positive rate for normal state in the network anomaly detection, a novel method of network anomaly detection based on constructing wavelet neural network (WNN) using quantum-behaved particle swarm optimization (QPSO) algorithm was proposed. The WNN was trained by QPSO. )