5 research outputs found
Chaotic Time Series Prediction using Spatio-Temporal RBF Neural Networks
Due to the dynamic nature, chaotic time series are difficult predict. In
conventional signal processing approaches signals are treated either in time or
in space domain only. Spatio-temporal analysis of signal provides more
advantages over conventional uni-dimensional approaches by harnessing the
information from both the temporal and spatial domains. Herein, we propose an
spatio-temporal extension of RBF neural networks for the prediction of chaotic
time series. The proposed algorithm utilizes the concept of time-space
orthogonality and separately deals with the temporal dynamics and spatial
non-linearity(complexity) of the chaotic series. The proposed RBF architecture
is explored for the prediction of Mackey-Glass time series and results are
compared with the standard RBF. The spatio-temporal RBF is shown to out perform
the standard RBFNN by achieving significantly reduced estimation error.Comment: Published in: 2018 3rd International Conference on Emerging Trends in
Engineering, Sciences and Technology (ICEEST). arXiv admin note: substantial
text overlap with arXiv:1908.0132