Enrique H. Ruspini

TL;DR: A new method of representation of the reduced data, based on the idea of “fuzzy sets,” is proposed to avoid some of the problems of current clustering procedures and to provide better insight into the structure of the original data.

TL;DR: In this paper, the authors considered the problem of decomposition of the probability density function of the original set into the weighted sum of the component fuzzy set densities, which is done by optimization of some functional defined over all possible fuzzy classifications.

TL;DR: The modifications presented here resulted in good fuzzy classifications in any, previously established, number of clusters using improved techniques for clustering data in fuzzy sets.

TL;DR: In this article, an equivalent formulation of a mixed boundary value problem as an initial-value problem is presented, accomplished by use of the theory of dual integral equations which allows the potential problem to be solved in terms of a Fredholm integral equation, followed by application of invariant imbedding.

TL;DR: In this article, a new method is presented for identifying parameters in a linear differential system arising, e.g., from compartment models in drug kinetics, using a series of recurrence relations that help reduce the computational load.