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Research and design of distributed training algorithm for neural networks

  • School of Computer Science and Technology, Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper presents a new Distributed training algorithm for Neural Networks based on multi-agents, which is created to solve the bottleneck of memory in current prediction of protein secondary structure program, and a chip training algorithm is proposed to work in the distributed environment to evolve the global optimum by competition from a group of neural network agents that have different sample chips for processing. The experimental results demonstrate that this method can effectively improve the convergent speed, has good expansibility, and can be applied to the prediction of protein secondary structure of middle and large size of amino-acid sequence.

Original languageEnglish
Title of host publication2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005
PublisherIEEE Computer Society
Pages4044-4049
Number of pages6
ISBN (Electronic)0780390911
ISBN (Print)078039092X, 9780780390928
DOIs
StatePublished - 2005
Externally publishedYes
EventInternational Conference on Machine Learning and Cybernetics, ICMLC 2005 - Guangzhou, China
Duration: 18 Aug 200521 Aug 2005

Publication series

Name2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005
Volume7

Conference

ConferenceInternational Conference on Machine Learning and Cybernetics, ICMLC 2005
Country/TerritoryChina
CityGuangzhou
Period18/08/0521/08/05

Keywords

  • Bioinformatics
  • Distributed neural network
  • Multi-agent
  • Training algorithm

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