Table 2 Type-2 fuzzy system
From: A new approach for tuning interval type-2 fuzzy knowledge bases using genetic algorithms
Year | Authors | Description | Reference |
|---|---|---|---|
2006 | D. Wu and W. W. Tan | Less computational expensive type-2 FLC is developed for real-time applications | [54] |
2006 | D. Wu and W. W. Tan | GAs are used to evolve type-2 FLC | [55] |
2007 | R. Sepulveda et al. | Feedback control systems for a non-linear plant using type-1 and type-2 fuzzy logic controllers | [56] |
2009 | R. Martinez et al. | Type-2 fuzzy systems and GAs are used to implement track controller for unicycle mobile robot | [57] |
2009 | M. H. F. Zarandi et al. | An interval type-2 fuzzy system has been developed for stock price analysis | [58] |
2011 | O. Castillo et al. | An interval type-2 fuzzy logic controller has been developed using evolutionary algorithms | [59] |
2012 | O. Castillo et al. | Ant colony optimization (ACO), particle swarm optimization (PSO), and GAs are used to optimize the MF parameters of a fuzzy logic controller | [60] |
2012 | D. Hidalgo et al. | A footprint of uncertainty (FoU)-based type-2 fuzzy system optimization has been developed | [61] |
2012 | O. Castillo and P. Mellin | A review on the optimization methods of type-2 fuzzy systems using bio-inspired computing | [62] |
2012 | R. Hosseini et al. | Automatic tuning and learning approach for type-2 fuzzy systems has been proposed applied to lung CAD classification system | [63] |