改进的PSO-BP网络模型

Jinxia Ren, Shuai Yang
{"title":"改进的PSO-BP网络模型","authors":"Jinxia Ren, Shuai Yang","doi":"10.1109/ISISE.2010.101","DOIUrl":null,"url":null,"abstract":"An improved network model to adjust weights of BP network based on particle swarm optimization(PSO) was proposed. The fuzzy control was used to assign the different weight to PSO and BP algorithm during different periods. PSO algorithm plays a main role in the previous evolution period, and BP algorithm plays a vital roal in later period. The model can overcome the slow convergence and easily getting into the local extremum of basic BP algorithm, and can also improve the learning ability and generalization ability with a higher precision. The simulation results show that the improved PSOBP network model has higher accuracy and quicker response than the traditional model.","PeriodicalId":206833,"journal":{"name":"2010 Third International Symposium on Information Science and Engineering","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"An Improved PSO-BP Network Model\",\"authors\":\"Jinxia Ren, Shuai Yang\",\"doi\":\"10.1109/ISISE.2010.101\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An improved network model to adjust weights of BP network based on particle swarm optimization(PSO) was proposed. The fuzzy control was used to assign the different weight to PSO and BP algorithm during different periods. PSO algorithm plays a main role in the previous evolution period, and BP algorithm plays a vital roal in later period. The model can overcome the slow convergence and easily getting into the local extremum of basic BP algorithm, and can also improve the learning ability and generalization ability with a higher precision. The simulation results show that the improved PSOBP network model has higher accuracy and quicker response than the traditional model.\",\"PeriodicalId\":206833,\"journal\":{\"name\":\"2010 Third International Symposium on Information Science and Engineering\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-12-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 Third International Symposium on Information Science and Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISISE.2010.101\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Third International Symposium on Information Science and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISISE.2010.101","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

摘要

提出了一种基于粒子群优化(PSO)的BP网络权值调整改进模型。采用模糊控制对PSO算法和BP算法在不同时间段赋予不同的权重。粒子群算法在进化前期起主要作用,BP算法在进化后期起重要作用。该模型克服了基本BP算法收敛慢、易陷入局部极值的缺点,提高了学习能力和泛化能力,精度较高。仿真结果表明,改进的PSOBP网络模型比传统模型具有更高的精度和更快的响应速度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Improved PSO-BP Network Model
An improved network model to adjust weights of BP network based on particle swarm optimization(PSO) was proposed. The fuzzy control was used to assign the different weight to PSO and BP algorithm during different periods. PSO algorithm plays a main role in the previous evolution period, and BP algorithm plays a vital roal in later period. The model can overcome the slow convergence and easily getting into the local extremum of basic BP algorithm, and can also improve the learning ability and generalization ability with a higher precision. The simulation results show that the improved PSOBP network model has higher accuracy and quicker response than the traditional model.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信