An inverse modeling using a five-layer perceptron

Author

Yamaguchi, Satoshi ; Tanaka, Miwako ; Itakura, Hidekiyo

Author_Institution

Dept. of Comput. Sci., Chiba Inst. of Technol., Narashino, Japan

Volume

3

fYear

1993

fDate

25-29 Oct. 1993

Firstpage

2803

Abstract

This paper shows a learning algorithm for an inverse model of a system using a five-layer perceptron. In the learning algorithm, two performance indexes are used: one is an index for the forward model of the system and the other is for the inverse model. The algorithm reduces these two performance indexes at the same time. As a result, the forward model and the inverse model are formed in the perceptron. The algorithm is applied to the learning of inverse kinematics and dynamics models of manipulators by computer simulations. By the simulation experiments, it is confirmed that the algorithm can learn the inverse models effectively.

Keywords

dynamics; inverse problems; kinematics; learning (artificial intelligence); manipulators; multilayer perceptrons; performance index; five-layer perceptron; inverse dynamics; inverse kinematics; inverse modeling; learning algorithm; manipulators; performance indexes; Computational modeling; Computer science; Computer simulation; Control system synthesis; Inverse problems; Jacobian matrices; Kinematics; Multilayer perceptrons; Neural networks; Performance analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on

Print_ISBN

0-7803-1421-2

Type

conf

DOI

10.1109/IJCNN.1993.714306

Filename

714306