Neurofuzzy cognitive maps with adaptive B-splines
Cognitive maps and time space maps make use of multidimensional scaling (MDS) techniques to analyze data relating to spatial and environmental preferences and perception. Perceptual configuration of the points is represented by a cognitive map with surface feature interpolation. In this paper we propose three procedures of MDS using Neurofuzzy adaptive modelling with B-splines for surface feature interpolation. The procedures are based on: (1) Hayashi's quantifying method of paired comparisons, (2) Torgerson's metrical MDS procedure and (3) Gradient descent method which is the basic learning method of adaptive systems such as the artificial neural networks and neurofuzzy modelling. In numerical examples, the resultant maps are compared and an application to sociometry analysis is presented. (C) 1998 Elsevier Science Inc. All rights reserved.
quantifying method neurofuzzy modelling sociometry multidimensional scaling technique