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Rule Base Simplification and Constrained Learning for Interpretability in TSK Neuro-Fuzzy Modelling

Rule Base Simplification and Constrained Learning for Interpretability in TSK Neuro-Fuzzy Modelling

Sharifa Rajab
Copyright: © 2020 |Volume: 9 |Issue: 2 |Pages: 28
ISSN: 2156-177X|EISSN: 2156-1761|EISBN13: 9781522598428|DOI: 10.4018/IJFSA.2020040102
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MLA

Rajab, Sharifa. "Rule Base Simplification and Constrained Learning for Interpretability in TSK Neuro-Fuzzy Modelling." IJFSA vol.9, no.2 2020: pp.31-58. http://doi.org/10.4018/IJFSA.2020040102

APA

Rajab, S. (2020). Rule Base Simplification and Constrained Learning for Interpretability in TSK Neuro-Fuzzy Modelling. International Journal of Fuzzy System Applications (IJFSA), 9(2), 31-58. http://doi.org/10.4018/IJFSA.2020040102

Chicago

Rajab, Sharifa. "Rule Base Simplification and Constrained Learning for Interpretability in TSK Neuro-Fuzzy Modelling," International Journal of Fuzzy System Applications (IJFSA) 9, no.2: 31-58. http://doi.org/10.4018/IJFSA.2020040102

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Abstract

Neuro-fuzzy systems based on a fuzzy model proposed by Takagi, Sugeno and Kang known as the TSK fuzzy model provide a powerful method for modelling uncertain and highly complex non-linear systems. The initial fuzzy rule base in TSK neuro-fuzzy systems is usually obtained using data driven approaches. This process induces redundancy into the system by adding redundant fuzzy rules and fuzzy sets. This increases complexity which adversely affects generalization capability and transparency of the fuzzy model being designed. In this article, the authors explore the potential of TSK fuzzy modelling in developing comparatively interpretable neuro-fuzzy systems with better generalization capability in terms of higher approximation accuracy. The approach is based on three phases, the first phase deals with automatic data driven rule base induction followed by rule base simplification phase. Rule base simplification uses similarity analysis to remove similar fuzzy sets and resulting redundant fuzzy rules from the rule base, thereby simplifying the neuro-fuzzy model. During the third phase, the parameters of membership functions are fine-tuned using a constrained hybrid learning technique. The learning process is constrained which prevents unchecked updates to the parameters so that a highly complex rule base does not emerge at the end of model optimization phase. An empirical investigation of this methodology is done by application of this approach to two well-known non-linear benchmark forecasting problems and a real-world stock price forecasting problem. The results indicate that rule base simplification using a similarity analysis effectively removes redundancy from the system which improves interpretability. The removal of redundancy also increased the generalization capability of the system measured in terms of increased forecasting accuracy. For all the three forecasting problems the proposed neuro-fuzzy system demonstrated better accuracy-interpretability tradeoff as compared to two well-known TSK neuro-fuzzy models for function approximation.

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