levy飞行改进pso_0801

Gesture Recognition using Neural Network

when transforming back the signal....,

说明：  AM MODULATION: It is matlab code for AM,DSBSC,SSBSC,SSB with carrier ,ISB, & multitone AM & their spectrum. AM is a technique used in electronic communication, most commonly for transmitting information via a radio carrier wave. AM works by varying the strength of the transmitted signal in relation to the information being sent. For example, changes in signal strength may be used to specify the sounds to be reproduced by a loudspeaker, or the light intensity of television pixels. Contrast this with frequency modulation, in which the frequency is varied, and phase modulation, in which the phase is varied.

实现射频中2*1，3*1,4*1空时分组码误码性能分析(Error performance analysis of 2*1, 3*1,4*1, and in RF)

bch channel coding matlab codes

The program computes the solution to a Laplace problem by the boundary element method.

The matlab code describes different schemes to store the sparse matrix of a given power system network.

Ensemble修改程序，包括源程序和调用程序及实例。(Ensemble modify the program, including the source and call procedures and examples.)

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)

求解庞加莱映射及吸因子程序 求解动力学微分方程(poljialai plot)