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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sg-ntu-dr.10356-1067662021-01-08T02:26:03Z RIT2FIS : a recurrent interval type 2 Fuzzy Inference System and its rule base estimation Samanta, Subhrajit Hartanto, Andre Pratama, Mahardhika Sundaram, Suresh Srikanth, Narasimalu School of Computer Science and Engineering International Joint Conference on Neural Networks (IJCNN 2019) Energy Research Institute @ NTU (ERI@N) Recurrent Neural Fuzzy Network Fuzzy Rule Base Estimation Engineering::Electrical and electronic engineering 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. Accepted version 2019-08-26T04:38:16Z 2019-12-06T22:17:58Z 2019-08-26T04:38:16Z 2019-12-06T22:17:58Z 2019 Conference Paper Samanta, S., Hartanto, A., Pratama, M., Sundaram, S., & Srikanth, N. (2019). RIT2FIS : a recurrent interval type 2 Fuzzy Inference System and its rule base estimation. International Joint Conference on Neural Networks (IJCNN 2019). https://hdl.handle.net/10356/106766 http://hdl.handle.net/10220/49775 en © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. 8 p. application/pdf |
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Recurrent Neural Fuzzy Network Fuzzy Rule Base Estimation Engineering::Electrical and electronic engineering |
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Recurrent Neural Fuzzy Network Fuzzy Rule Base Estimation Engineering::Electrical and electronic engineering Samanta, Subhrajit Hartanto, Andre Pratama, Mahardhika Sundaram, Suresh Srikanth, Narasimalu RIT2FIS : a recurrent interval type 2 Fuzzy Inference System and its rule base estimation |
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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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School of Computer Science and Engineering |
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School of Computer Science and Engineering Samanta, Subhrajit Hartanto, Andre Pratama, Mahardhika Sundaram, Suresh Srikanth, Narasimalu |
format |
Conference or Workshop Item |
author |
Samanta, Subhrajit Hartanto, Andre Pratama, Mahardhika Sundaram, Suresh Srikanth, Narasimalu |
author_sort |
Samanta, Subhrajit |
title |
RIT2FIS : a recurrent interval type 2 Fuzzy Inference System and its rule base estimation |
title_short |
RIT2FIS : a recurrent interval type 2 Fuzzy Inference System and its rule base estimation |
title_full |
RIT2FIS : a recurrent interval type 2 Fuzzy Inference System and its rule base estimation |
title_fullStr |
RIT2FIS : a recurrent interval type 2 Fuzzy Inference System and its rule base estimation |
title_full_unstemmed |
RIT2FIS : a recurrent interval type 2 Fuzzy Inference System and its rule base estimation |
title_sort |
rit2fis : a recurrent interval type 2 fuzzy inference system and its rule base estimation |
publishDate |
2019 |
url |
https://hdl.handle.net/10356/106766 http://hdl.handle.net/10220/49775 |
_version_ |
1688654662844022784 |