Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/106766
Title: RIT2FIS : a recurrent interval type 2 Fuzzy Inference System and its rule base estimation
Authors: Samanta, Subhrajit
Hartanto, Andre
Pratama, Mahardhika
Sundaram, Suresh
Srikanth, Narasimalu
Keywords: Recurrent Neural Fuzzy Network
Fuzzy Rule Base Estimation
Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: 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).
Abstract: 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.
URI: https://hdl.handle.net/10356/106766
http://hdl.handle.net/10220/49775
Rights: © 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.
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