摘要
针对基于ELM学习算法的单隐含层前馈神经网络需要较大的网络规模、影响网络泛化能力的问题,基于新皮层内神经元的连接特点,在前馈神经网络中引入不同层神经元之间的跨越连接,构造跨越前馈神经网络。同时,基于ELM学习算法设计适用于跨越前馈神经网络的学习算法,提高网络的学习能力。Image Segmentation多分类问题及直线一级倒立摆系统控制的实验研究表明,该方法能够提高网络的学习能力,具有明显的优势。
The single-hidden layer feedforward neural network （SLFN） based on ELM needs larger-scale network structure to solve practical applications, which will influence the generalization capability. In order to solve the problem, a span feedforward neural network （SFN） is proposed based on the characteristic of neocortex neurons. The span connections between any two non-adjacent layers were introduced into this network. At the same time, an improved learning algorithm based on ELM is presented, the proposed approach of span feedforward neural network and improved ELM learning algorithm is used for some benchmark problems. The study on Image Segmentation multi-classification problem and linear 1-stage inverted pendulum system control show that the proposed approach performs better than SLFN and ELM, and it could improve the learning ability of the nerwork.
出处
《现代电子技术》
2013年第15期108-111,共4页
Modern Electronics Technique
基金
国家863计划项目(2012A006402)
关键词
神经网络
跨越连接
极速学习机
倒立摆系统
neural network
span connection
extreme learning machine
inverted pendulum system
作者简介
石红伟 男，1983年出生，河北人，硕士，助理工程师。研究方向包括人工智能、系统建模与控制等。
