@inproceedings{ae432d6f5a1742a18ad6421e4dc3973c,
title = "A Novel Self-Organizing Emotional CMAC Network for Robotic Control",
abstract = "This paper proposes a self-organizing control system for uncertain nonlinear systems. The proposed neural network is composed of a conventional brain emotional learning network (BEL) and a cerebellar model articulation controller network (CMAC). The input value of the network is feed to a BEL channel and a CMAC channel. The output of the network is generated by the comprehensive action of the two channels. The structure of the network is dynamic, using a self-organizing algorithm allows increasing or decreasing weight layers. The parameters of the proposed network are on-line tuned by the brain emotional learning rules; the updating rules of CMAC and the robust controller are derived from the Lyapunov function; in addition, stability analysis theory is used to guaranty the proposed controller's convergence. A simulated mobile robot is applied to prove the effectiveness of the proposed control system. By comparing with the performance of other neural-network-based control systems, the proposed network produces better performance.",
author = "Juncheng Zhang and Quanfeng Li and Xiang Chang and Fei Chao and Lin, {Chih Min} and Longzhi Yang and Huynh, {Tuan Tu} and Ling Zheng and Changle Zhou and Changjing Shang",
note = "Funding Information: This work was supported by the National Natural Science Foundation of China (No. 61673322, 61673326, and 91746103), the Fundamental Research Funds for the Central Universities (No. 20720190142), and the European Union{\textquoteright}s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 663830. Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Joint Conference on Neural Networks, IJCNN 2020 ; Conference date: 19-07-2020 Through 24-07-2020",
year = "2020",
month = sep,
day = "28",
doi = "10.1109/IJCNN48605.2020.9207710",
language = "English",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "IEEE Press",
booktitle = "2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings",
address = "United States of America",
}