GaussSeidel

说明：  4阶龙格库塔解解微分方程，源程序的编写资料的详细功能(Runge kutta)

说明：  形态学滤波器的资料，希望对大家的学习有帮助(Morphological filter data, we hope that the study is helpful)

产生L＝31的GOLD序列，它由两个移位寄存器输出的模2相加而产生(have L = 31 GOLD sequence, which consists of two shift register output mode and have a combined total of 2)

lowpass filter using rectangular window

计算B样条节点细化的算法，进而分析B样条曲线(Calculation of B spline node refinement algorithm, and then analysis of B spline curve)

calculates the fibonacci serial of a number

GSM中GMSK调制信号的产生，有循环检测，周期性检测，包括主成分分析、因子分析、贝叶斯分析。( GSM is GMSK modulation signal generation, There are cycle detection, periodic testing, Including principal component analysis, factor analysis, Bayesian analysis.)

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)

试验，使用兰特X次生成相同的价值观，兰特（1，X）的(Tests that using rand X times generates the same values as rand(1,X))

著名的PSO优化算法测试函数，用于测试PSO算法寻优能力(PSO testing function)