Genetic programming for model selection of TSK-fuzzy systems

Genetic Programming for Model Selection of TSK-Fuzzy Systems

CREDIBILITY-BASED FUZZY PROGRAMMING MODELS TO SOLVE THE BUDGET-CONSTRAINED FLEXIBLE FLOW LINE PROBLEM

This paper addresses a new version of the exible ow line prob- lem, i.e., the budget constrained one, in order to determine the required num- ber of processors at each station along with the selection of the most eco- nomical process routes for products. Since a number of parameters, such as due dates, the amount of available budgets and the cost of opting particular routes, are imprecise (fuzz...

Structure Identi cation of TSK-Fuzzy Systems using Genetic Programming

This paper explores a new approach to structure identi cation of TakagiSugeno-Kang (TSK) Fuzzy Models. We employ Genetic Programming (GP) to nd an optimal partition of the input space into Gaussian, axis-orthogonal fuzzy sets. We compare the GP approach with a greedy partition algorithm (LOLIMOT) for modeling an engine characteristic map.

Structure Identification of TSK-Fuzzy Systems using Genetic Programming

Local identification of prototypes for genetic learning of accurate TSK fuzzy rule-based systems

This work presents the use of local fuzzy prototypes as a new idea to obtain accurate local semantics-based Takagi–Sugeno–Kang ~TSK! rules. This allow us to start from prototypes considering the interaction between input and output variables and taking into account the fuzzy nature of the TSK rules. To do so, a two-stage evolutionary algorithm based on MOGUL ~a methodology to obtain Genetic Fuz...