On the General Motion-Planning Problem with Two Degrees of Freedom.
On the Hopfield algorithm. Foundations and examples.
On the k-reversibility of finite automata.
On the learning of weights in some aggregation operators: the weigthed mean and OWA operators.
We study the determination of weights for two types of aggregation operators: the weighted mean and the OWA operator. We assume that there is at our disposal a set of examples for which the outcome of the aggregation operator is known. In the case of the OWA operator, we compare the results obtained by our method with another one in the literature. We show that the optimal weighting vector is reached with less cost.
On the Number of Critical Free Contacts of a Convex Polygonal Object Moving in Two-Dimensional Polygonal Space.
On the orthogonalization of arbitrary Boolean formulae.
On the singular decomposition of matrices.
On the size of minimal unsatisfiable formulas.
On the Threshold of Bounded Pseudo-Distances.
On the viability of an algebraic approach to non-monotonic reasoning.
One implementation of PL prover algorithm
Ontologies and soft computing in flexible querying
Operating on formal concept abstraction.
The subject of this paper regards a procedure to obtain the abstract form of concepts, directly from their most natural form, thus these can be efficiently learned and the possibility of operating formally on them is reached. The achievement of said type of form results also useful to compute conceptual parameters symbolic and numerical in nature.
Optimal Allocation of Renewable Energy Parks: A Two–stage Optimization Model
Applied research into Renewable Energies raises complex challenges of a technological, economical or political nature. In this paper, we address the techno−economical optimization problem of selecting locations of wind and solar Parks to be built in Egypt, such that the electricity demand is satisfied at minimal costs. Ultimately, our goal is to build a decision support tool that will provide private and governmental investors into renewable energy systems, valuable insights to make informed short...
Optimal estimators in learning theory
This paper is a survey of recent results on some problems of supervised learning in the setting formulated by Cucker and Smale. Supervised learning, or learning-from-examples, refers to a process that builds on the base of available data of inputs and outputs , i = 1,...,m, a function that best represents the relation between the inputs x ∈ X and the corresponding outputs y ∈ Y. The goal is to find an estimator on the base of given data that approximates well the regression function of...
Optimal learning systems
Optimisation en classification automatique et reconnaissance des formes
Optimisation globale par stratégie d'évolution
Optimización multimodal mediante autómatas borrosos.
Optimization of the maximum likelihood estimator for determining the intrinsic dimensionality of high-dimensional data
One of the problems in the analysis of the set of images of a moving object is to evaluate the degree of freedom of motion and the angle of rotation. Here the intrinsic dimensionality of multidimensional data, characterizing the set of images, can be used. Usually, the image may be represented by a high-dimensional point whose dimensionality depends on the number of pixels in the image. The knowledge of the intrinsic dimensionality of a data set is very useful information in exploratory data analysis,...