Similarity analysis for robot motions using an FNN learning mechanism

Kuu-Young Young; Jyh Kao Wang

doi:10.1109/CDC.1997.657691

Similarity analysis for robot motions using an FNN learning mechanism

Kuu-Young Young^*, Jyh Kao Wang

^*Corresponding author for this work

Department of Electrical and Computer Engineering

Research output: Contribution to journal › Conference article › peer-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 language	English
Article number	657691
Pages (from-to)	2523-2528
Number of pages	6
Journal	Proceedings of the IEEE Conference on Decision and Control
Volume	3
DOIs	https://doi.org/10.1109/CDC.1997.657691
State	Published - 12 Dec 1997
Event	Proceedings of the 1997 36th IEEE Conference on Decision and Control. Part 1 (of 5) - San Diego, CA, USA Duration: 10 Dec 1997 → 12 Dec 1997

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10.1109/CDC.1997.657691

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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.",

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AU - Wang, Jyh Kao

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N2 - 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.

AB - 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.

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SP - 2523

EP - 2528

JO - Proceedings of the IEEE Conference on Decision and Control

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T2 - Proceedings of the 1997 36th IEEE Conference on Decision and Control. Part 1 (of 5)

Y2 - 10 December 1997 through 12 December 1997

ER -

Similarity analysis for robot motions using an FNN learning mechanism

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