Similarity analysis for robot motions using an FNN learning mechanism

Kuu-Young Young*, Jyh Kao Wang

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

Learning controllers are usually subordinate to conventional controllers in governing multiple-joint robot motion, in spite of their ability to generalize, because learning-space complexity and motion variety require them to consume excessive amount of memory. We propose using a Fuzzy Neural Network (FNN) to learn and analyze robot motions so they can be classified according to similarity. After classification, the learning controller can then be designed to govern robot motions according to their similarities without consuming excessive memory resources.

Original languageEnglish
Pages (from-to)2523-2528
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
Volume3
DOIs
StatePublished - 1 Dec 1997
EventProceedings of the 1997 36th IEEE Conference on Decision and Control. Part 1 (of 5) - San Diego, CA, USA
Duration: 10 Dec 199712 Dec 1997

Fingerprint Dive into the research topics of 'Similarity analysis for robot motions using an FNN learning mechanism'. Together they form a unique fingerprint.

Cite this