A probabilistic, recurrent, fuzzy neural network for processing noisy time-series data

Yong Li*, Richard Gault, T.M McGinnity

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


The rapidly increasing volumes of data and the need for big data analytics has emphasised the need for algorithms that can accommodate incomplete or noisy data. The concept of recurrency is an important aspect of signal processing, providing greater robustness and accuracy in many situations, such as biological signal processing. Probabilistic fuzzy neural networks (PFNN) have shown potential in dealing with uncertainties associated with both stochastic and non-stochastic noise simultaneously. Previous research work on this topic has addressed either the fuzzy-neural aspects or alternatively the probabilistic aspects, but there currently does not exist a probabilistic fuzzy neural algorithm with recurrent feedback.
In this paper a probabilistic fuzzy neural network with a recurrent probabilistic generation module (designated PFNN-R) is proposed to enhance and extend the ability of the PFNN to accommodate noisy data. A back-propagation based mechanism, which is used to shape the distribution of the probabilistic density function of the fuzzy membership, is also developed. The motivation of the work was to develop an approach which provides an enhanced capability to accommodate various types of noisy data. We apply the algorithm to a number of benchmark problems and demonstrate via simulation results that the proposed technique incorporating recurrency advances probabilistic, fuzzy neural networks’ ability in modelling time series data with high intensity, random noise
Original languageEnglish
JournalIEEE Transactions on Neural Networks and Learning Systems
Publication statusAccepted - 12 Feb 2021

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