These papers describe state-of-the-art work in the field of neuro-fuzzy pattern recognition. They cover feature selection, classification, classifier training, and clustering. Also included are applications of neuro-fuzzy systems in speech recognition, land mine detection, and more.

Methodology: simultaneous feature analysis and system identification in a neuro-fuzzy framework, N.R. Pal and D. Chakraborty; neuro-fuzzy model for unsupervised feature extraction with real-life applications, R.K. De et al; a computational-intelligence-based approach to decision support, M.B. Gorzalczany; clustering problem using fuzzy C-means algorithms and unsupervised neural networks, J.-S. Lin; automatic training of min-max classifiers, A. Rizzi; granular computing in pattern recognition, W. Pedrycz and G. Vukovich; ART-based model set for pattern recognition - FasArt family, G.I. Sainz Palmero et al. Applications: a methodology and a system for adaptive speech recognition in a noisy environment based on adaptive noise cancellation and evolving fuzzy neural networks, N. Kasabov and G. Iliev; neural versus heuristic development of Choquet fuzzy integral fusion algorithms for land mine detection, P.D. Gader et al; automatic segmentation of multi-spectral MR brain images using a neuro-fuzzy algorithm, S.Y. Lee et al; vision-based neuro-fuzzy control of autonomous lane following vehicle, Y.-J. Ryoo.