A model-constrained adaptive sampling methodology is proposed for reduction of large-scale systems with high-dimensional parametric input spaces. Our model reduction method uses a ...
In this paper, we propose two novel techniques, which successfully address several major problems in the field of particle swarm optimization (PSO) and promise a significant breakt...
Serkan Kiranyaz, Turker Ince, E. Alper Yildirim, M...
In this paper, in order to reduce the explosive increase of the search space as the input dimension grows, we present a new representation method for the structure of fuzzy rules, ...
In this paper, we present a new cost model for nearest neighbor search in high-dimensional data space. We first analyze different nearest neighbor algorithms, present a generaliza...
Abstract--We propose an automatic method for measuring content-based music similarity, enhancing the current generation of music search engines and recommender systems. Many previo...