| 改进人工蜂群优化的极限学习机 |
| |
| 引用本文： | 毛羽,毛力,杨弘,肖炜. 改进人工蜂群优化的极限学习机[J]. 传感器与微系统, 2018, 0(4): 116-120. DOI: 10.13873/J.1000-9787(2018)04-0116-05 |
| |
| 作者姓名： | 毛羽 毛力 杨弘 肖炜 |
| |
| 作者单位： | 轻工过程先进控制教育部重点实验室江南大学物联网工程学院,江苏无锡,214122中国水产科学研究院淡水渔业研究中心,江苏无锡,214081 |
| |
| 基金项目： | 轻工过程先进控制教育部重点实验室开放课题资助(江南大学)项目，江南大学自主科研计划重点资助项目，国家青年科学基金资助项目，现代农业产业技术体系专项资金资助项目 |
| |
| 摘 要： | 针对极限学习机(ELM)在训练过程中需要大量隐含层节点的问题,提出了差分进化与克隆算法改进人工蜂群优化的极限学习机(DECABC-ELM),在人工蜂群算法的基础上,引入了差分进化算法的差分变异算子和免疫克隆算法的克隆扩增算子,改进了人工蜂群收敛速度慢等缺点,使用改进的人工蜂群算法计算ELM的隐含层节点参数.将算法应用于回归和分类数据集,并与其他算法进行比较,获得了良好的效果.
|
| 关 键 词： | 人工蜂群算法 极限学习机 单隐含层前馈神经网络 artificial bee colony（ABC）algorithm extreme learning machine single hidden layer feedforward neural networks |
Extreme learning machine optimized by improved ABC |
| |
| MAO Yu,MAO Li,YANG Hong,XIAO Wei. Extreme learning machine optimized by improved ABC[J]. Transducer and Microsystem Technology, 2018, 0(4): 116-120. DOI: 10.13873/J.1000-9787(2018)04-0116-05 |
| |
| Authors: | MAO Yu MAO Li YANG Hong XIAO Wei |
| |
| Abstract: | Aiming at problem that extreme learning machine(ELM)needs numerous hidden layer nodes in training process,a new improved differential evolution and clone artificial bee colony(ABC)optimized extreme learning machine(DECABC-ELM)is proposed. In DECABC-ELM,differential evolution mutation operator of differential evolution algorithm and clone-increase operator of immune clonal algorithm are introduced into ABC algorithm to improve the slow convergence speed of it,then the improved ABC is used to calculate the hidden layer node parameters of ELM.DECABC-ELM are used in regression and classification data set and compare with other algorithms,it shows that DECABC-ELM performs better than other algorithms. |
| |
| Keywords: | |
| 本文献已被 万方数据 等数据库收录！ |
|
