A genetic-algorithm-based method for tuning fuzzy logic controllers
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Bee Colony Optimization metaheuristic for fuzzy membership functions tuning
2020, Expert Systems with ApplicationsMULAN: Evaluation and ensemble statistical inference for functional connectivity
2018, NeuroImageCitation Excerpt :We used a genetic algorithm because it can handle the high degree of nonlinearity imposed by our strategy (traditional linear optimization tools cannot be used). Genetic algorithms have demonstrated the ability, efficiency and robustness in handling complex search spaces (Bancaud et al., 1970; Gürocak, 1999; Herrera et al., 1995) and achieving the complex objectives we set (the calculated connectivity graph needs to be similar to the ground-truth). However, other algorithms could be used and implemented.
Development of a Reinforcement Learning-based Evolutionary Fuzzy Rule-Based System for diabetes diagnosis
2017, Computers in Biology and MedicineAn improved genetic-fuzzy system for classification and data analysis
2017, Expert Systems with ApplicationsCitation Excerpt :In the other approach, where interpretable Mamdani fuzzy systems are usually employed, some flexibility is added to the system’s structure to expand the search space and thus produce more accurate system (Casillas et al., 2003). Among the strategies applied to enhance the accuracy are: tuning the membership functions by changing the definition values of the parameters or their types (Casillas, Cordon, del Jesus, & Herrera, 2005; Cordon & Herrera, 1997; Gacto, Alcala, & Herrera, 2010; Gürocak, 1999; Jin, von Seelen, & Sendhoff, 1999; Nauck, 2000; Shi, Eberhart, & Chen, 1999), using linguistic modifiers in the rules that allow for more flexibility without losing the interpretability (Cordon, José del Jesus, & Herrera, 1998; Fernandez, del Jesus, & Herrera, 2010; Gonzalez & Perez, 1999; Herrera & Martinez, 2000) and learning the granularity of the fuzzy partitions to choose the level which gives more accuracy (Espinosa & Vandewalle, 2000; Gacto et al., 2010). Multi-objective evolutionary algorithms (MOEAs) have been extensively used in the context of interpretability to solve the problem of the interpretability-accuracy trade-off in fuzzy rule-based systems.
Adaptive fuzzy multivariable controller design based on genetic algorithm for an air handling unit
2015, EnergyCitation Excerpt :A method is presented in Ref. [17] for tuning fuzzy control rules by genetic algorithms to make the fuzzy logic control systems behave as closely as possible to the operator or expert behavior in a control process. An adaptive learning algorithm based on genetic algorithm for the tuning fuzzy rule base is presented in Ref. [18]. Two tuning algorithms namely Lateral and LA-based algorithms have been presented in Ref. [19] for the adaptation of fuzzy controllers.
Speed control of permanent magnet synchronous motors using fuzzy controller based on genetic algorithms
2012, International Journal of Electrical Power and Energy SystemsCitation Excerpt :Overview of the studies on genetic fuzzy systems can be found in Cordon et al. [10] studies. Gürocak [12], in his study, proposed a method based on GA for generating rule base of a FLC. Shi and Eberhart [13] added types of membership functions into the used GA chromosome structure in addition to fuzzy membership function and rule base and hence, they formed a different chromosome structure.