Análisis de regresión con datos imprecisos: Un nuevo enfoque que utiliza distancias difusas y sus aplicaciones

[ES] El análisis de regresión es una herramienta estadística potente con muchas aplicaciones en diferentes áreas. Este problema en un entorno difuso ha sido tratado en la literatura desde diferentes puntos de vista y teniendo en cuenta una variedad de datos de entrada/salida (reales o difusos). En esta memoria se presenta una nueva metodología basada en una familia de medidas de distancia difusas entre números difusos arbitrarios que se define utilizando algunas de las características posibilistas y geométricas más importantes de cualquier número difuso. A continuación, en este contexto y utilizando el método de mínimos cuadrados se propone una nueva técnica de regresión difusa para resolver problemas lineales y no lineales. Este proceso de estimación, en general, se puede considerar fácil de aplicar en la práctica y no se limita a números difusos triangulares. Finalmente, algunos ejemplos numéricos ilustran su utilidad y aplicabilidad.[EN]Regression analysis is a powerful statistical tool which has many applications in different areas. This problem under a fuzzy environment has been treated in the literature from different points of view and considering a variety of input/ output data (crisp or fuzzy). In this study we present a new methodology based on a family of fuzzy distance measures between arbitrary fuzzy numbers which involve some of the most important possibilistic and geometric characteristics of any fuzzy number. Next in this context and using the method of least squares we propose a new fuzzy regression technique to solve linear and non-linear problems. This estimation process, in general, can be considered easy to apply in practical situations and it is not limited to triangular fuzzy numbers. Finally, numerical examples are provided to illustrate its usefulness and applicability.Tesis Univ. Jaén. Departamento de Estadistica e Investigación Operativ

Empirical comparison of the performance of location estimates of fuzzy number-valued data

© Springer Nature Switzerland AG 2019. Several location measures have already been proposed in the literature in order to summarize the central tendency of a random fuzzy number in a robust way. Among them, fuzzy trimmed means and fuzzy M-estimators of location extend two successful approaches from the real-valued settings. The aim of this work is to present an empirical comparison of different location estimators, including both fuzzy trimmed means and fuzzy M-estimators, to study their differences in finite sample behaviour.status: publishe

dARTMAP: A Neural Network for Fast Distributed Supervised Learning

Distributed coding at the hidden layer of a multi-layer perceptron (MLP) endows the network with memory compression and noise tolerance capabilities. However, an MLP typically requires slow off-line learning to avoid catastrophic forgetting in an open input environment. An adaptive resonance theory (ART) model is designed to guarantee stable memories even with fast on-line learning. However, ART stability typically requires winner-take-all coding, which may cause category proliferation in a noisy input environment. Distributed ARTMAP (dARTMAP) seeks to combine the computational advantages of MLP and ART systems in a real-time neural network for supervised learning, An implementation algorithm here describes one class of dARTMAP networks. This system incorporates elements of the unsupervised dART model as well as new features, including a content-addressable memory (CAM) rule for improved contrast control at the coding field. A dARTMAP system reduces to fuzzy ARTMAP when coding is winner-take-all. Simulations show that dARTMAP retains fuzzy ARTMAP accuracy while significantly improving memory compression.National Science Foundation (IRI-94-01659); Office of Naval Research (N00014-95-1-0409, N00014-95-0657

Using fuzzy numbers and OWA operators in the weighted average and its application in decision making

Se presenta un nuevo método para tratar situaciones de incertidumbre en los que se utiliza el operador OWAWA (media ponderada – media ponderada ordenada). A este operador se le denomina operador OWAWA borroso (FOWAWA). Su principal ventaja se encuentra en la posibilidad de representar la información incierta del problema mediante el uso de números borrosos los cuales permiten una mejor representación de la información ya que consideran el mínimo y el máximo resultado posible y la posibilidad de ocurrencia de los valores internos. Se estudian diferentes propiedades y casos particulares de este nuevo modelo. También se analiza la aplicabilidad de este operador y se desarrolla un ejemplo numérico sobre toma de decisiones en la selección de políticas fiscalesWe present a new approach for dealing with an uncertain environment when using the ordered weighted averaging – weighted averaging (OWAWA) operator. We call it the fuzzy OWAWA (FOWAWA) operator. The main advantage of this new aggregation operator is that it is able to represent the uncertain information with fuzzy numbers. Thus, we are able to give more complete information because we can consider the maximum and the minimum of the problem and the internal information between these two results. We study different properties and different particular cases of this approach. We also analyze the applicability of the new model and we develop a numerical example in a decision making problem about selection of fiscal policies

Measuring Partial Membership in Categories: Alternative Tools

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Pattern recognition for Space Applications Center director's discretionary fund

Results and conclusions are presented on the application of recent developments in pattern recognition to spacecraft star mapping systems. Sensor data for two representative starfields are processed by an adaptive shape-seeking version of the Fc-V algorithm with good results. Cluster validity measures are evaluated, but not found especially useful to this application. Recommendations are given two system configurations worthy of additional study