文档介绍：会计学
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粒子群与BP神经网络结合
智能优化算法：
最优化问题是指在一定的约束条件下，决定某个或某些可控制的因素应有的合理取值，使所选定的目标达到最优的问题。
人们借鉴自然现象，提出了模拟退火法(SA)、遗传算法(GA)、神经网络法(ＮＮ)；人们通过学****生物的生活规律，提出了蚁群算法(ACO)、粒子群优化算法(PSO)。
人们将这些模仿自然现象及生物体的各种原理和机理的方法，称为智能优化算法。
粒子群优化算法
粒子群算法的起源
粒子群优化算法(Particle Swarm Optimization，PSO），是一种启发式群智能进化计算技术，由Kennedy and Eberhart于1995年提出，来源于对鸟群捕食的行为的研究，是一种基于迭代的优化工具。
James Kennedy received the . degree from theUniversity of North Carolina, Chapel Hill, in is with the . Department of Labor, Washington,DC. He is a Social Psychologist who has been working with the particle swarm algorithm since 1994. He has published dozens of articles and chapters on particle swarms and related topics, in computer science and social science journals and proceedings. He is a coauthor of Swarm Intelligence (San Mateo, CA: Morgan Kaufmann, 2001), with . Eberhart and Y. Shi, now in its third printing.
Russell C. Eberhart (M’88–SM’89–F’01) received the . degree in electrical engineering from Kansas State University, is the Chair and Professor of Electrical and Computer Engineering, Purdue School of Engineering and Technology, Indiana University–Purdue University Indianapolis (IUPUI),Indianapolis, IN. He is coeditor of Neural Network PC Tools(1990),coauthor of Computational Intelligence PC Tools (1996), coauthor of Swarm Intelligence(2001), Computational Intelligence: Concepts to Implementations(2004). He has published over 120 technical . Eberhart was awarded the IEEE Third Millenium Medal. In 2002, he became a Fellow of the American Institute for Medical and Biological Engineering.
近年PSO方面文献的数量
粒子群算法的主要应用
(一)函数优化
(二)神经网络训练
(三)工程领域应用
(四)PSO算法还在生物工程、电磁学、数据挖掘等很多领域都取得了较好的效果。
原始粒子群优化算法
标准粒子群优化算法
原始粒子群优化算法
Pbest:个体极值(粒子自身所找到的当前最优解)
Gbest:全局极值(整个群体当前找到的最优解)
设D维搜索空间中，有M个粒子，其中第i个的位置是Xi =(xi1,xi2,...xiD)，速度为Vi =(vi1,vi2,...,viD)，i = 1,2，…，M。搜索到的历史最优位置为Pi =(pi1,pi2,...,piD)，整个粒子群体搜索到的最优位置为Pg =(pg1,pg2,...,pgD)。Knenedy和Eberhrtn最早提出的PSO