4 research outputs found
A Robust Variable Step Size Fractional Least Mean Square (RVSS-FLMS) Algorithm
In this paper, we propose an adaptive framework for the variable step size of
the fractional least mean square (FLMS) algorithm. The proposed algorithm named
the robust variable step size-FLMS (RVSS-FLMS), dynamically updates the step
size of the FLMS to achieve high convergence rate with low steady state error.
For the evaluation purpose, the problem of system identification is considered.
The experiments clearly show that the proposed approach achieves better
convergence rate compared to the FLMS and adaptive step-size modified FLMS
(AMFLMS).Comment: 15 pages, 3 figures, 13th IEEE Colloquium on Signal Processing & its
Applications (CSPA 2017
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