Your English writing platform
Discover LudwigExact(24)
In additions, the proposed package has good extensibilities to establish piecewise linear membership functions and to approximate non-linear membership functions.
The fuzzy inference system utilizing non-linear membership functions is seen to perform slightly better than that with linear membership functions for the input variables.
Linearization technique is used in order to transform the non-linear membership functions into equivalent linear membership functions and then normalize them.
Therefore, we select linear membership functions for the proactive and reactive controllers.
The linear membership functions are adopted to reduce the complexity of the model.
Linear membership functions are defined for each objective depending on their nature.
Similar(36)
Linear and nonlinear membership functions are used and the results are compared with direct numerical simulation results.
Additionally, linear or nonlinear membership functions of objective functions can be selected by designer depending on the practical requirements of design objectives.
In the proposed procedure, the output definitions of fuzzy subsystems are derived in a unique parametric form in terms of the inversion variable by using parameters of linear definition of membership functions.
The area enclosed by the fuzzy number will be dealt with as the solution set of a system of linear inequalities, where the membership functions represent the constraints.
In this study, the area enclosed by the fuzzy number will be dealt with as the solution set of a system of linear inequalities, where the membership functions represent the constraints.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com