Elicitation and fine-tuning of fuzzy control rules using symbiotic evolution

Distinguishability quantification of fuzzy sets

“…[1,5,[9][10][11][12][14][15][16][17]19,20,22,26]). Informally speaking, distinguishability is a relation between fuzzy sets (defined on the same Universe of Discourse) directly related to their overlapping: the more overlapping two fuzzy sets are, the less distinguishable they become.…”

“…As a consequence, most strategies of model building that adopt similarity measures for interpretability enhancement are based on massive search algorithms such as Genetic Algorithms [15,20,23], Evolution Strategies [13], Symbiotic Evolution [11], Coevolution [19], or Multi-Objective Genetic Optimization [12]. Alternatively, distinguishability improvement is realized in a separate design stage, often after some data driven procedure like clustering, in which similar fuzzy sets are usually merged together [17,22].…”

Fuzzy inference system for software product family process evaluation

“…In case of Gaussian function, the merged fuzzy set can be constructed by calculating C and r as given by Eqs. (5) and (6), as follows [14]:…”

Sliding Mode Control of Half-car Active Suspension

“…In [10], a mathematical models of a seven-degree of freedom sus-pension system based on the whole vehicle are established and the fuzzy controller of vehicle semi-active suspension system is de-signed. A large class of fuzzy approches for vehicle suspension sys-tem are developed [11], [12], [13], [14], [15], [16]. A lot of researches have suggested control methods for vehicle suspension systems which combined two intelligent controls, fuzzy logic and neural network control [17], [18].…”