郑州大学学报（理学版）2013，Vol.45Issue(1)：100-104,5.DOI:10.3969/j.issn/1671-6841.2013.01.024
一种基于粒子群优化的极限学习机
A New Extreme Learning Machine Optimized by PSO
摘要
Abstract
Extreme learning machine (ELM) was a new type of feedforward neural network. Compared with traditional single hidden layer feedforward neural networks, ELM possessed higher training speed and smaller error. Due to random input weights and hidden biases, ELM might need numerous hidden neurons to achieve a reasonable accuracy. A new ELM learning algorithm, which was optimized by the particle swarm optimization (PSO) , was proposed. PSO algorithm was used to select the input weights and bias of hidden layer, then the output weights could be calculated. To test the validity of proposed method, two simulation experiments were drawn on the approximation curves of the Sine function. Experimental results showed that the proposed algorithm achieved better performance with less hidden neurons than other similar methods.关键词
粒子群/极限学习机/隐含层节点Key words
particle swarm optimization/ extreme learning machine/ hidden neurons分类
信息技术与安全科学引用本文复制引用
王杰,毕浩洋..一种基于粒子群优化的极限学习机[J].郑州大学学报（理学版）,2013,45(1):100-104,5.基金项目
国家自然科学基金资助项目,编号60905039/F030507. （）
