A Meta-cognitive Recurrent Fuzzy Inference System with Memory Neurons (McRFIS-MN) and its fast learning algorithm for time series forecasting
In this paper, a Meta-cognitive Recurrent Fuzzy Inference System is proposed where recurrence is brought using Memory type Neurons (McRFIS-MN) to retain the effect of all past instances, while the meta-cognition component is employed to control the learning process, by deciding what-to-learn, when-t...
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| Format: | Conference Paper |
| Language: | English |
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2020
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| Online Access: | https://hdl.handle.net/10356/144532 |
| _version_ | 1826129009903140864 |
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| author | Samanta, Subhrajit Ghosh, Shubhangi Sundaram, Suresh |
| author2 | Interdisciplinary Graduate School (IGS) |
| author_facet | Interdisciplinary Graduate School (IGS) Samanta, Subhrajit Ghosh, Shubhangi Sundaram, Suresh |
| author_sort | Samanta, Subhrajit |
| collection | NTU |
| description | In this paper, a Meta-cognitive Recurrent Fuzzy Inference System is proposed where recurrence is brought using Memory type Neurons (McRFIS-MN) to retain the effect of all past instances, while the meta-cognition component is employed to control the learning process, by deciding what-to-learn, when-to-learn and how-to-learn from the training data. The McRFIS-MN model has five layers, and Memory Neurons (MN) are employed only in the layers handling crisp values. The antecedent parameters are set randomly while only the consequent weights of the network are updated using a one-shot type projection based learning algorithm through time (PBLT) which makes the learning very fast. The performance evaluation of McRFIS-MN has been carried out using benchmark problems in the areas of nonlinear system identification and time-series forecasting. The results are evaluated against some of the most popular neural fuzzy methods and the obtained results indicate that McRFIS-MN performs better in terms of speed while achieving better or similar accuracy. |
| first_indexed | 2024-10-01T07:34:12Z |
| format | Conference Paper |
| id | ntu-10356/144532 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:34:12Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1445322023-03-05T16:27:16Z A Meta-cognitive Recurrent Fuzzy Inference System with Memory Neurons (McRFIS-MN) and its fast learning algorithm for time series forecasting Samanta, Subhrajit Ghosh, Shubhangi Sundaram, Suresh Interdisciplinary Graduate School (IGS) 2018 IEEE Symposium Series on Computational Intelligence (SSCI) Energy Research Institute @ NTU (ERI@N) Engineering::Computer science and engineering Recurrent Neural Fuzzy Network Time Series Analysis In this paper, a Meta-cognitive Recurrent Fuzzy Inference System is proposed where recurrence is brought using Memory type Neurons (McRFIS-MN) to retain the effect of all past instances, while the meta-cognition component is employed to control the learning process, by deciding what-to-learn, when-to-learn and how-to-learn from the training data. The McRFIS-MN model has five layers, and Memory Neurons (MN) are employed only in the layers handling crisp values. The antecedent parameters are set randomly while only the consequent weights of the network are updated using a one-shot type projection based learning algorithm through time (PBLT) which makes the learning very fast. The performance evaluation of McRFIS-MN has been carried out using benchmark problems in the areas of nonlinear system identification and time-series forecasting. The results are evaluated against some of the most popular neural fuzzy methods and the obtained results indicate that McRFIS-MN performs better in terms of speed while achieving better or similar accuracy. Accepted version 2020-11-11T06:10:34Z 2020-11-11T06:10:34Z 2019 Conference Paper Samanta, S., Ghosh, S., & Sundaram, S. (2019). A Meta-cognitive Recurrent Fuzzy Inference System with Memory Neurons (McRFIS-MN) and its fast learning algorithm for time series forecasting. Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI). doi:10.1109/SSCI.2018.8628936 https://hdl.handle.net/10356/144532 10.1109/SSCI.2018.8628936 en © 2018 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. The published version is available at: https://doi.org/10.1109/SSCI.2018.8628936 application/pdf |
| spellingShingle | Engineering::Computer science and engineering Recurrent Neural Fuzzy Network Time Series Analysis Samanta, Subhrajit Ghosh, Shubhangi Sundaram, Suresh A Meta-cognitive Recurrent Fuzzy Inference System with Memory Neurons (McRFIS-MN) and its fast learning algorithm for time series forecasting |
| title | A Meta-cognitive Recurrent Fuzzy Inference System with Memory Neurons (McRFIS-MN) and its fast learning algorithm for time series forecasting |
| title_full | A Meta-cognitive Recurrent Fuzzy Inference System with Memory Neurons (McRFIS-MN) and its fast learning algorithm for time series forecasting |
| title_fullStr | A Meta-cognitive Recurrent Fuzzy Inference System with Memory Neurons (McRFIS-MN) and its fast learning algorithm for time series forecasting |
| title_full_unstemmed | A Meta-cognitive Recurrent Fuzzy Inference System with Memory Neurons (McRFIS-MN) and its fast learning algorithm for time series forecasting |
| title_short | A Meta-cognitive Recurrent Fuzzy Inference System with Memory Neurons (McRFIS-MN) and its fast learning algorithm for time series forecasting |
| title_sort | meta cognitive recurrent fuzzy inference system with memory neurons mcrfis mn and its fast learning algorithm for time series forecasting |
| topic | Engineering::Computer science and engineering Recurrent Neural Fuzzy Network Time Series Analysis |
| url | https://hdl.handle.net/10356/144532 |
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