NDLI: Identification of robot manipulators using neural networks and genetic programming

Content Provider	IEEE Xplore Digital Library
Author	Kiguchi, K. Hyeon-Ho Jang Fukuda, T.
Copyright Year	1999
Description	Author affiliation: Dept. of Adv. Syst. Control Eng., Saga Univ., Japan (Kiguchi, K.)
Abstract	An evolving neural networks (NNs) based identification method is proposed using genetic programming (GP). Advantages of both NNs and GP are combined in the proposed method. Identification of unknown/uncertain robot manipulators is realized by using the adaptation ability of NNs, and the architecture of the NNs is evolved by using the GP technique. Consequently, evolution of the NN architecture and adaptation of its weights are carried out in the proposed method. In the proposed GP, the architecture of each individual in the population is the same as a NN. The adaptation process of each NN (each individual in the population) to the unknown/uncertain robot manipulator is carried out using the back-propagation learning algorithm during the fitness evaluation process of each NN in GP. Therefore, the NN, which shows better adaptation to the unknown/uncertain robot manipulator, results in better fitness in the proposed method. In order to avoid frequent disruption of the important subtree of the NN caused by crossover operators, the worst subtree of one selected NN is replaced with the best subtree of the other NN. The back-propagated errors during the adaptation process are used for evaluation of the subtrees of each NN. This strategy makes the evolution of NN architecture more efficient than traditional GP. The effectiveness of the proposed identification method has been evaluated with a 2DOF planar robot manipulator.
Starting Page	802
Ending Page	806
File Size	318229
Page Count	5
File Format	PDF
ISBN	0780357310
ISSN	1062922X
DOI	10.1109/ICSMC.1999.812509
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-10-12
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Manipulators Neural networks Genetic programming Genetic engineering Electronic mail Nonlinear systems Service robots Control engineering Electrical equipment industry Industrial control
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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