Gu, X., Angelov, P., Han, J. et al. (1 more author) (2023) Multilayer evolving fuzzy neural networks. IEEE Transactions on Fuzzy Systems, 31 (12). pp. 4158-4169. ISSN 1063-6706
Abstract
It is widely recognised that learning systems have to go deeper to exchange for more powerful representation learning capabilities in order to precisely approximate nonlinear complex problems. However, the best known computational intelligence approaches with such characteristics, namely, deep neural networks, are often criticised for lacking transparency. In this paper, a novel multilayer evolving fuzzy neural network (MEFNN) with a transparent system structure is proposed. The proposed MEFNN is a meta-level stacking ensemble learning system composed of multiple cascading evolving neuro-fuzzy inference systems (ENFISs), processing input data layer-by-layer to automatically learn multi-level nonlinear distributed repre- sentations from data. Each ENFIS is an evolving fuzzy system capable of learning from new data sample by sample to self- organise a set of human-interpretable IF-THEN fuzzy rules that facilitate approximate reasoning. Adopting ENFIS as its ensemble component, the multilayer system structure of MEFNN is flexible and transparent, and its internal reasoning and decision-making mechanism can be explained and interpreted to/by humans. To facilitate information exchange between different layers and at- tain stronger representation learning capability, MEFNN utilises error backpropagation to self-update the consequent parameters of the IF-THEN rules of each ensemble component based on the approximation error propagated backward. To enhance the capability of MEFNN to handle complex problems, a nonlinear activation function is introduced to modelling the consequent parts of the IF-THEN rules of ENFISs, thereby empowering both the representation and the reflection of nonlinearity in the resulting fuzzy outputs. Numerical examples on a wide variety of challenging (benchmark and real-world) classification and regression problems demonstrate the superior practical performance of MEFNN, revealing the effectiveness and validity of the proposed approach.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | evolving fuzzy system; fuzzy neural network; self-organised; stacking ensemble |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 18 May 2023 10:58 |
Last Modified: | 03 Oct 2024 15:14 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/TFUZZ.2023.3276263 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:199315 |