Frequency selection with oscillatory neurons for engine misfire detection

Dae Eun Kim, Jaehong Park

Research output: Contribution to conferencePaperpeer-review

3 Citations (Scopus)


Detecting novelties over time series data is of practical interest in many signal processing applications. Especially engine misfire detection is one of the great issues in automobile systems to inform incomplete engine exhaustion to cause environmental problem and also to guarantee safe operation of vehicles. It requires continuous monitoring of the system in real-time to detect deviations from the normal signal patterns. This paper presents a special frequency selection method based on recurrent neural networks consisting of oscillatory neurons, and applies the method with genetic algorithm to engine misfire detection problem by observing engine speed in automobile system.

Original languageEnglish
Number of pages4
Publication statusPublished - 1999
EventInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
Duration: 1999 Jul 101999 Jul 16


OtherInternational Joint Conference on Neural Networks (IJCNN'99)
CityWashington, DC, USA

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence


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