JACIII Vol.23 No.5 pp. 838-846
doi: 10.20965/jaciii.2019.p0838


Neural Network Based Online Anthropomorphic Performance Decision-Making Approach for Dual-Arm Dulcimer Playing Robot

Ting Fei*,**, Xin Chen*,**,†, and Li Zhou***

*School of Automation, China University of Geosciences
No.388 Lumo Road, Hongshan District, Wuhan 430074, China

**Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems
No.388 Lumo Road, Hongshan District, Wuhan 430074, China

***School of Arts and Communication, China University of Geosciences
No.388 Lumo Road, Hongshan District, Wuhan 430074, China

Corresponding author

August 24, 2018
March 18, 2019
September 20, 2019
decision-making, anthropomorphic, online, dual-arm, dulcimer playing robot

A neural network based online anthropomorphic performance decision-making approach is described for a dual-arm dulcimer playing robot. Because it is difficult to extract experiential rules manually to describe the decision behavior of a human playing a dulcimer, the proposed method relies on the self-learning function of a artificial neural network (ANN). The training data of the network consists of three types of information: the note pitch of adjacent notes, time interval in a piece of music, and decision results in actual performance processes of human beings. A decision-making approach, devised through combining the well-trained ANN with music for which performance decisions were required, is then applied. The numerical results show that, for several pieces of music with different characteristics, the accuracy and precision of the decision results are always relatively high, which verifies the practicability and good generalizability of the method.

Anthropomorphic dual-arm dulcimer playing robot

Anthropomorphic dual-arm dulcimer playing robot

Cite this article as:
T. Fei, X. Chen, and L. Zhou, “Neural Network Based Online Anthropomorphic Performance Decision-Making Approach for Dual-Arm Dulcimer Playing Robot,” J. Adv. Comput. Intell. Intell. Inform., Vol.23 No.5, pp. 838-846, 2019.
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