Artículo

Dynamic Random Fuzzy Cognitive Maps

Mapas Cognitivos Difusos Aleatorios Dinámicos

José Aguilar

CEMISID, Departamento. de Computación Facultad de Ingeniería Universidad de los Andes Av. Tulio Febres. Mérida, Venezuela. E–mail: aguilar@cemisid.ing.ula.ve

Article received on June 28, 2002
Accepted on May 4, 2004

Abstract

A fuzzy cognitive map is a graphical means of representing arbitrary complex models of interrelations between concepts. The purpose of this paper is to describe a dynamic/adaptive fuzzy cognitive map based on the random neural network model. Previously, we have developed a random fuzzy cognitive map and illustrated its application in the modeling of processes. The adaptive fuzzy cognitive map changes its fuzzy causal web as causal patterns change and as experts update their causal knowledge. Our model carries out inferences via numerical calculation instead of symbolic deduction. We show how the adaptive/dynamic random fuzzy cognitive map can reveal implications of models composed of dynamic processes.

Keywords: Random Neural Network, Fuzzy Logic, Fuzzy Cognitive Maps, Dynamic Systems.

Resumen

Un Mapa Cognitivo Difuso es un medio gráfico de representación de modelos complejos de interrelaciones entre conceptos. El propósito de este artículo es describir un Mapa Cognitivo Difuso Dinámico/Adaptivo basado en el Modelo de Redes Neuronales Aleatorias. En trabajos previos, nosotros hemos desarrollado un Mapa Cognitivo Difuso Aleatorio y mostrado su aplicación en el modelado de procesos. Nuestro modelo realiza inferencias a través de cálculos numéricos en vez de deducciones simbólicas. Ahora bien, el Mapa Cognitivo Difuso Adaptivo cambia su red de relaciones causales difusas como un patrón causal cambia y un experto actualiza su conocimiento causal. Nosotros mostramos cómo el Mapa Cognitivo Difuso Dinámico/Adaptivo puede ser usado para describir implicaciones en el modelado de procesos dinámicos.

Palabras Clave: Redes Neuronales Aleatorias, Lógica Difusa, Mapas Cognitivos Difusos, Sistemas Dinámicos.

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Acknowledgment

This work was partially supported by CONICIT grant "Agenda Petróleo: 97003817", CDCHT–ULA grant I–621–98–02–A and CeCalCULA (High Performance Computing Center of Venezuela).

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