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A Parallel Levenberg-Marquardt Algorithm for Recursive Neural Network in a Robot Control System

A Parallel Levenberg-Marquardt Algorithm for Recursive Neural Network in a Robot Control System

Wei Wang, Yunming Pu, Wang Li
Copyright: © 2018 |Volume: 12 |Issue: 2 |Pages: 16
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781522543039|DOI: 10.4018/IJCINI.2018040103
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MLA

Wang, Wei, et al. "A Parallel Levenberg-Marquardt Algorithm for Recursive Neural Network in a Robot Control System." IJCINI vol.12, no.2 2018: pp.32-47. http://doi.org/10.4018/IJCINI.2018040103

APA

Wang, W., Pu, Y., & Li, W. (2018). A Parallel Levenberg-Marquardt Algorithm for Recursive Neural Network in a Robot Control System. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 12(2), 32-47. http://doi.org/10.4018/IJCINI.2018040103

Chicago

Wang, Wei, Yunming Pu, and Wang Li. "A Parallel Levenberg-Marquardt Algorithm for Recursive Neural Network in a Robot Control System," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 12, no.2: 32-47. http://doi.org/10.4018/IJCINI.2018040103

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Abstract

This article has the purpose of overcoming the shortcomings of the recursive neural network learning algorithm and the inherent delay problem on the manipulator master control system. This is by analyzing the shortcomings of LM learning algorithms based on DRNN network, an improved parallel LM algorithm is proposed. The parallel search of the damping coefficient β is found in order to reduce the number of iterations of the loop, and the algorithm is used to decompose the parameter operation and the matrix operation into the processor (core), thereby improve the learning convergence speed, and control the scale of the delay. The simulation results show that the pro-posed algorithm is feasible.

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