dtw2

2*1 alamouti code, MIMO Rayleigh fading Channel Capacity, and waterfilling

欧拉法解二阶微分方程组的源代码 以某一特例为代表(Euler)

说明：  fir滤波器具体参数： 带通范围：0.35pi－0.8pi，1dB 阻带为<0.2pi,>0.8pi,60dB(fir filter specific parameters : Bandpass Scope : 0.35pi- 0.8pi, resistance bands 1dB)

it is an .rar file. it contains some source code of matlab

4-9-1的结构ga-lm的bp算法 有示例数值 不是拟合而是预测(4-9-1 structure of ga-lm bp algorithm is not fit but there are examples of numerical prediction)

Kalman filter that can be simulated under windws(Kalman filter that can be simulated under w indws)

说明：  基于HSM（harmonic-to-subharmonic）的基音提取算法。本算法通过对HSM建模，消除了基音提取过程中基音减半或增倍的现象，大大提高了准确性能。(Based on HSM (harmonic-to-subharmonic) of the pitch extraction algorithm. HSM model by the algorithm, the extraction process to eliminate the pitch halving or pitch doubling phenomenon, greatly improving the accuracy of performance.)

利用模式理论，对给定几何尺寸以及折射率分布的光纤进行模式求解与绘图，该程序给出LP01模的求解与绘图。(the LP01 mode of the fiber. )

code for OSPF hope u like it

说明：  近年来，基于启发式的多目标优化技术得到了很大的发展，研究表明该技术比经典方法更实用和高效。有代表性的多目标优化算法主要有NSGA、NSGA-II、SPEA、SPEA2、PAES和PESA等。粒子群优化(PSO)算法是一种模拟社会行为的、基于群体智能的进化技术，以其独特的搜索机理、出色的收敛性能、方便的计算机实现，在工程优化领域得到了广泛的应用，多目标PSO(MOPSO)算法应用到了不同的优化领域[9~11]，但存在计算复杂度高、通用性低、收敛性不好等缺点。 多目标粒子群（MOPSO）算法是由CarlosA. Coello Coello等在2004年提出来的(In recent years, heuristic-based multi-objective optimization technology has been greatly developed, and research shows that this technology is more practical and efficient than classical methods. Representative multi-objective optimization algorithms mainly include NSGA, NSGA-II, SPEA, SPEA2, PAES and PESA. Particle Swarm Optimization (PSO) algorithm is an evolutionary technology based on swarm intelligence that simulates social behavior. With its unique search mechanism, excellent convergence performance, and convenient computer implementation, it has been widely used in the field of engineering optimization. The objective PSO (MOPSO) algorithm is applied to different optimization fields [9~11], but it has shortcomings such as high computational complexity, low versatility, and poor convergence. The multi-objective particle swarm optimization (MOPSO) algorithm was proposed by Carlos A. Coello Coello et al. in 2004)