RIT2FIS : a recurrent interval type 2 Fuzzy Inference System and its rule base estimation
Two of the major challenges associated with time series modelling are handling uncertainty present in the data and tracing its dynamical behaviour. A Recurrent Interval Type 2 Fuzzy Inference System or RIT2FIS is presented in this paper. RIT2FIS adopts an interval type 2 fuzzy inference mechanis...
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Main Authors: | , , , , |
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Other Authors: | |
Format: | Conference or Workshop Item |
Language: | English |
Published: |
2019
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Subjects: | |
Online Access: | https://hdl.handle.net/10356/106766 http://hdl.handle.net/10220/49775 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | Two of the major challenges associated with time
series modelling are handling uncertainty present in the data
and tracing its dynamical behaviour. A Recurrent Interval Type
2 Fuzzy Inference System or RIT2FIS is presented in this paper.
RIT2FIS adopts an interval type 2 fuzzy inference mechanism for
superior handling of uncertainty. The memory neurons employed
in its hidden and output layer, retain the temporal information,
making RIT2FIS highly proficient in tracing system dynamics
at a granular level. RIT2FIS also benefits from incorporating
a k-means algorithm inspired approach to cluster the data in
an unsupervised manner. An ’Elbow Method’ is utilized next
to determine the optimal clustering which is then employed
as the optimal fuzzy rule base for RIT2FIS, eliminating the
necessity of expert knowledge for fuzzy initiation. The antecedent
and consequent parameters of RIT2FIS are updated using a
gradient descent based backpropagation through time algorithm
where the learning is made self-regulatory to avoid over-fitting
and ensure generalization. Performance of RIT2FIS is evaluated
against popular neuro-fuzzy methods on different benchmark
and real-world time series problems which distinctly indicates
an improved accuracy and a parsimonious rule base. |
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