Abstract
This work aims to propose the chaotic time series prediction based on autoregressive moving average (ARMA) model via fuzzy adaptive step size–normalized least mean square (FASS-NLMS) algorithm. In the FASS-NLMS algorithm, which is the estimation algorithm used for estimating the weight vector of ARMA model, the step size is adapted via Mamdani fuzzy inference system (MFIS). In the MFIS used, the input variables are the squared residual error and the normalized time instant; the output variable is the adapted step size. The proposed methodology was evaluated in the Lorenz time series prediction. Furthermore, through statistical metrics, the obtained ARMA model was evaluated through results obtained for the training stage and prediction stage, in which it was possible to note the satisfactory performance of the proposed methodology.
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Noronha, R.P. (2022). Chaotic Lorenz Time Series Prediction via NLMS Algorithm with Fuzzy Adaptive Step Size. In: Das, B., Patgiri, R., Bandyopadhyay, S., Balas, V.E. (eds) Modeling, Simulation and Optimization. Smart Innovation, Systems and Technologies, vol 292. Springer, Singapore. https://doi.org/10.1007/978-981-19-0836-1_34
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DOI: https://doi.org/10.1007/978-981-19-0836-1_34
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