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arXiv · 1908.01321

Spatio-Temporal RBF Neural Networks

Shujaat Khan·Jawwad Ahmad·Alishba Sadiq·Imran Naseem·Muhammad Moinuddin
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Abstract

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.

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Shujaat Khan, Jawwad Ahmad, Alishba Sadiq, Imran Naseem, Muhammad Moinuddin. 2019-08-04. Spatio-Temporal RBF Neural Networks. https://doi.org/10.1109/iceest.2018.8643322

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