A constrained Takagi–Sugeno fuzzy system that allows for better interpretation and analysis
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Cited by (33)
Evolving fuzzy reasoning approach using a novel nature-inspired optimization tool
2021, Expert Systems with ApplicationsCitation Excerpt :However, it suffers from poor readability. The readability of this type of model can be enhanced by an easy-to-understand statement of the outputs of the rules (Fiordaliso, 2001). On the other hand, a fuzzy-reasoning model with Mamdani approach is found to be less accurate but with the better readability compared to that of Takagi and Sugeno’s approach.
Linguistic composition based modelling by fuzzy networks with modular rule bases
2015, Fuzzy Sets and SystemsCitation Excerpt :Furthermore, dimensionality represents an obstacle to efficiency because it is more difficult to reduce the amount of computations in a FID sequence for a large number of rules [33–36]. Finally, structure is an obstacle to transparency as it is harder to understand the behaviour of a black-box model that does not reflect the interactions among subsystems [37–40]. This paper introduces an advanced theoretical framework for NFS as a novel type of fuzzy system.
Generating flexible convex hyper-polygon validity regions via sigmoid-based membership functions in TS modeling
2015, Applied Soft Computing JournalCitation Excerpt :In a third strategy to provide more flexible validity regions, MFs are considered as the product of two or more nonlinear functions. In [21], a generalized TS fuzzy model is introduced whose MFs are equal to the product of sigmoid functions and other un-even nonlinear functions which are computed from clustering results. In [22], a variant of TS fuzzy model is introduced whose MFs are equal to the product of sigmoid functions with a quadratic functions, where the sigmoid functions provide flat top hyper-rectangle validity regions while quadratic functions provide flexible validity regions for local linear models.
Knowledge-based parameter identification of TSK fuzzy models
2010, Applied Soft Computing JournalModelling and optimization of fluid dispensing for electronic packaging using neural fuzzy networks and genetic algorithms
2010, Engineering Applications of Artificial IntelligenceCitation Excerpt :Kang et al. (1993) have proved that the TSK fuzzy system approach outperforms statistical regression and polynomial models in both correlation and prediction in modelling of highly nonlinear systems. Compared with conventional approaches of fuzzy logic, recent research has shown that neural fuzzy systems can achieve better performance, at least in mathematical function approximation, compared with the conventional approaches with the same number of fuzzy sets used in input variables (Fiordaliso, 2001). After developing process models, process optimization can be carried out to determine the optimal/proper process parameters of manufacturing processes.