An Intelligent Spatial Proximity System Using Neurofuzzy Classifiers and Contextual Information

Abstract

In this paper, we propose a novel approach to reasoning with the concepts of spatial proximity. The approach is based on contextual information and uses a neurofuzzy classifier to handle the uncertainty aspect of proximity. Neurofuzzy systems are a combination of neural networks and fuzzy systems and effectively incor porate the advantages of both techniques. Although fuzzy systems are focused on knowledge rep re sen ta tion, they do not allow for the estimation of membership functions. Conversely, neuronal networks use powerful learning techniques but are not able to explain how results are obtained. Neurofuzzy systems ben e fit from both techniques by using neuronal network training data to generate membership functions and by using fuzzy rules to represent expert knowledge. Moreover, contextual information is collected from a knowl edge base. The neurofuzzy classifier is used to compute the membership function parameters of the spatial relations fuzzy quantifiers. The complete solution that we propose is integrated in a geographic information sys tem (GIS), enhanced with proximity-reasoning. Our approach is used in the telecommunication domain and particularly in fiber optic monitoring systems. In such systems, a user needs to qualify the distance between events reported by sensors and the surrounding objects of the environment, in order to form spatiotemporal pat terns. These patterns are defined to help users making decisions pertaining to operations, such as optimizing the assignment of emergency crews.