Improved Brain Emotional Learning Algorithm for Fast Classification

MEI Ying; TAN Guan-zheng; LIU Zhen-tao

doi:10.3969/j.issn.0372-2112.2017.11.013

您当前的位置：

首页 >

文章列表页 >

Improved Brain Emotional Learning Algorithm for Fast Classification

更新时间：2025-07-16

- Improved Brain Emotional Learning Algorithm for Fast Classification
- Acta Electronica Sinica Vol. 45, Issue 11, Pages: 2663-2670(2017)
- 作者机构：
  
  1. 中南大学信息科学与工程学院,湖南,长沙,410083
  2. 湖南文理学院电气与信息工程学院,湖南,常德,415000
  3. 中国地质大学自动化学院,湖北,武汉,430074
  4. 中南大学信息科学与工程学院,湖南,长沙,410083
  5. 湖南文理学院电气与信息工程学院,湖南,常德,415000
  6. 中国地质大学自动化学院,湖北,武汉,430074
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61403422);Science Research Program of Education Department of Hunan Province (No17C1084)
- DOI：10.3969/j.issn.0372-2112.2017.11.013
  CLC： TP18
- Published Online：25 November 2017，
  
  Published：2017
- 稿件说明：
移动端阅览
MEI Ying, TAN Guan-zheng, LIU Zhen-tao. Improved Brain Emotional Learning Algorithm for Fast Classification[J]. Acta Electronica Sinica, 2017, 45(11): 2663-2670.
DOI：

MEI Ying, TAN Guan-zheng, LIU Zhen-tao. Improved Brain Emotional Learning Algorithm for Fast Classification[J]. Acta Electronica Sinica, 2017, 45(11): 2663-2670. DOI： 10.3969/j.issn.0372-2112.2017.11.013.

摘要

为了提高数据分类的快速性与准确性，本文在大脑情感学习（Brain Emotional Learning，BEL）模型的基础上，结合遗传算法（Genetic Algorithm，GA），提出了一种基于GA-BEL的快速分类改进算法.BEL模型根据大脑中杏仁体和眶额皮质之间相互学习的神经生物学原理建立，模拟了情感刺激在大脑短反射通路中被快速处理的过程.因此，基于BEL模型的网络运算速度快.进一步采用遗传算法优化BEL网络权值，提高其分类正确率.在UCI数据集上的对比实验结果表明，无论对于小样本还是大样本数据集，较其他分类算法，GA-BEL算法均有较高的分类正确率和计算效率.

Abstract

An improved fast classification algorithm based on Brain Emotional Learning (BEL) and Genetic Algorithm (GA) is proposed to enhance the accuracy and efficiency.Inspired by the neurobiology research of emotional learning mechanism in amygdala and orbitofrontal cortex

the BEL model is constructed to mimic the mechanism of emotional stimulus processing in human brain.For the short path in the emotional brain

BEL can speed up the learning process.Furthermore

the learning weights in BEL are optimized by GA in order to improve the accuracy.Experiments using UCI datasets are performed

by which the results show that GA-BEL classification obtains higher accuracy and less computing time compared to other classifiers

in both small and large sample datasets.

关键词

Keywords

references

Views

388

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Genetic Algorithm Based MIMO Equalization Parameter Optimization Technology for Mode-Division Multiplexed System

An Absorbing Structure Covering the Whole UHF Band

A Consortium Blockchain Based Reliable Task Offloading Approach in Edge Computing

An Approach to Multi-Path Coverage Testing Based on Key Edge Probability and Path Layer Proximity

One Hybrid Strategy for Automatic Placement and Routing of QCA Circuit

Related Author

ZHAO Tian-feng

WEN Feng

FENG Bian-xia

WU Bao-jian

XU Bo

QIU Kun

WU Chang-ying

GAO Yang

Related Institution

Key Laboratory of Optical Fiber Sensing and Communication Networks, Ministry of Education, University of Electronics Science and Technology of China

School of Electronics and Information， Northwestern Polytechnical University

Key Laboratory for Novel Software Technology， Nanjing University

Guangdong Provincial Laboratory of Artificial Intelligence and Digital Economy

College of Big Data and Intelligent Engineering， Southwest Forestry University

⁰