Herein, we propose a spatio-temporal extension of RBFNN for nonlinear system identification problem. The proposed algorithm employs the concept of time-space orthogonality and separately models the dynamics and nonlinear complexities of the system. The proposed RBF architecture is explored for the estimation of a highly nonlinear system and results are compared with the standard architecture for both the conventional and fractional gradient decent-based learning rules. The spatio-temporal RBF is shown to perform better than the standard and fractional RBFNNs by achieving fast convergence and significantly reduced estimation error.
@article{arxiv.1908.01321,
title = {Spatio-Temporal RBF Neural Networks},
author = {Shujaat Khan and Jawwad Ahmad and Alishba Sadiq and Imran Naseem and Muhammad Moinuddin},
journal= {arXiv preprint arXiv:1908.01321},
year = {2019}
}
Comments
Published in 2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology (ICEEST)