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Neuro-fuzzy rule generation: survey in soft computing framework
Abstract
The present article is a novel attempt in providing an exhaustive survey of neuro-fuzzy rule generation algorithms. Rule generation from artificial neural networks is gaining in popularity in recent times due to its capability of providing some insight to the user about the symbolic knowledge embedded within the network. Fuzzy sets are an aid in providing this information in a more human comprehensible or natural form, and can handle uncertainties at various levels. The neuro-fuzzy approach, symbiotically combining the merits of connectionist and fuzzy approaches, constitutes a key component of soft computing at this stage. To date, there has been no detailed and integrated categorization of the various neuro-fuzzy models used for rule generation. We propose to bring these together under a unified soft computing framework. Moreover, we include both rule extraction and rule refinement in the broader perspective of rule generation. Rules learned and generated for fuzzy reasoning and fuzzy control are also considered from this wider viewpoint. Models are grouped on the basis of their level of neuro-fuzzy synthesis. Use of other soft computing tools like genetic algorithms and rough sets are emphasized. Rule generation from fuzzy knowledge-based networks, which initially encode some crude domain knowledge, are found to result in more refined rules. Finally, real-life application to medical diagnosis is provided.
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Cites
Survey and critique of techniques for extracting rules from trained artificial neural networks
Andrews, R., Diederich, J., & Tickle, A. B. (1995) · Knowledge-Based Systems · doi:10.1016/0950-7051(96)81920-4 · Cited by 1,225
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Weron, R. (2014) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2014.08.008 · Cited by 1,646
A Survey on Neural Network Interpretability
Zhang, Y., Tino, P., Leonardis, A., & Tang, K. (2021) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2021.3100641 · Cited by 923
A survey on industrial applications of fuzzy control
Precup, R.-E., & Hellendoorn, H. (2011) · Computers in Industry · doi:10.1016/j.compind.2010.10.001 · Cited by 532
Fuzzy Broad Learning System: A Novel Neuro-Fuzzy Model for Regression and Classification
Feng, S., & Chen, C. L. P. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2857815 · Cited by 490
FURIA: an algorithm for unordered fuzzy rule induction
Hühn, J., & Hüllermeier, E. (2009) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-009-0131-8 · Cited by 441
Metadata
- Authors
- Mitra, S., & Hayashi, Y.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 2000-05
- Volume
- 11
- Issue
- 3
- Pages
- 748-768
- ISSN
- 1045-9227 (ISSN-L)
- OpenAlex
- W2159111290
- Cited by
- 726
- References
- 1
- FWCI
- 21.66
- Type
- journal article
- Language
- en
- Open access
- closed
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- Mitra, S., & Hayashi, Y. (2000). Neuro-fuzzy rule generation: survey in soft computing framework. IEEE Transactions on Neural Networks. https://doi.org/10.1109/72.846746
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
