GA-fuzzy modeling and classification: Complexity and performance

Abstract—The use of genetic algorithms (GAs) and other evolutionary optimization methods to design fuzzy rules for systems modeling and data classification have received much attention in recent literature. Authors have focused on various aspects of these randomized techniques, and a whole scale of algorithms have been proposed. We comment on some recent work and describe a new and efficient two-step approach that leads to good results for function approximation, dynamic systems modeling and data classification problems. First fuzzy clustering is applied to obtain a compact initial rule-based model. Then this model is optimized by a real-coded GA subjected to constraints that maintain the semantic properties of the rules. We consider four examples from the literature: a synthetic nonlinear dynamic systems model, the iris data classification problem, the wine data classification problem, and the dynamic modeling of a diesel engine turbocharger. The obtained results are compared to other recently proposed methods. Index Terms—Classification, dynamic systems, fuzzy clustering, real-coded genetic algorithm (GA), Takagi–Sugeno–Kang (TSK) fuzzy model. I

Some learning cases in the Port of Rotterdam

This paper present several cases in the Port of Rotterdam where both in the design and construction phase several different did not marched as expected. These cases are interesting for both the design as well the construction phase of a project. People will make mistakes however the mistakes describes in this paper have to be judged in time as not always everything was understood during the design and construction and the people of that time still these huge structures.Hydraulic Structures and Flood Ris