Evolutionary hexapod robot gait control using a new recurrent neural network learned through group-based hybrid metaheuristic algorithm-Reference-Cited by-同舟云学术

Evolutionary hexapod robot gait control using a new recurrent neural network learned through group-based hybrid metaheuristic algorithm

Published:2018-07-06 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Genetic and Evolutionary Computation Conference Companion
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Author:

Juang Chia-Feng¹,Chang Yu-Cheng¹,Chung I-Fang²

Affiliation:

1. National Chung-Hsing University, Taichung, Taiwan

2. National Yang-Ming University, Taipei, Taiwan

Funder

Ministry of Science and Technology, Taiwan

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3205651.3205671

Reference9 articles.

1. A Survey on CPG-Inspired Control Models and System Implementation

2. Biped locomotion control with evolved adaptive center-crossing continuous time recurrent neural networks

3. C. F. Juang and Y. T. Yeh. 2018. Multi-objective evolution of biped robot gaits using advanced continuous ant-colony optimized recurrent neural networks. IEEE Trans. Cyber. In Press (2018). C. F. Juang and Y. T. Yeh. 2018. Multi-objective evolution of biped robot gaits using advanced continuous ant-colony optimized recurrent neural networks. IEEE Trans. Cyber. In Press (2018).

4. Evolving Dynamical Neural Networks for Adaptive Behavior

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