CORE
🇺🇦
make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
Improvement of 'winner takes all' neural network training for the purpose of diesel engine fault clustering
Authors
Gibadullin R.
Iliukhin A.
Publication date
1 January 2017
Publisher
Abstract
© 2016 IEEE.To create a diagnostic system for diesel engines, it is necessary to analyze a huge amount of data obtained from the automated test systems for diesel engines. Therefore, it is worth to implement the analysis with the help of an artificial neural network. The application of the artificial neural network for diesel engine fault clustering allows reducing the amount of stored data by creation of a knowledge database for the weighting factors. Self-training makes it possible to revise this database, improving the accuracy of clustering, and to modify network structure, in case the new types of faults will appear. The modified neural network training algorithm involves the usage of input vector data originally found within each cluster group as the initial weighting factors. This algorithm allows decreasing the load on the computing devices by reducing the number of training cycles in comparison with other existing algorithms. The efficiency of the method can be improved with a larger number of samples and dimensions of input and output parameters
Similar works
Full text
Open in the Core reader
Download PDF
Available Versions
Kazan Federal University Digital Repository
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:dspace.kpfu.ru:net/145346
Last time updated on 07/05/2019