Neural networks and fuzzy techniques are among the most promising approaches to pattern recognition. Neuro-fuzzy systems aim at combining the advantages of the two paradigms. This book is a collection of papers describing state-of-the-art work in this emerging field. It covers topics such as feature selection, classification, classifier training, and clustering. Also included are applications of neuro-fuzzy systems in speech recognition, land mine detection, medical image analysis, and autonomous vehicle control. The intended audience includes graduate students in computer science and related fields, as well as researchers at academic institutions and in industry. Machine generated contents note: Simultaneous Feature Analysis and System Identification in a Neuro-Fuzzy Framework 3 N. R. Pal and D. Chakraborty Neuro-Fuzzy Model for Unsupervised Feature Extraction with Real Life Applications 23 R. K. De et al. A Computational-Intelligence-Based Approach to Decision Support 51 M. B. Gorzalczany Clustering Problem Using Fuzzy C-Means Algorithms and Unsupervised Neural Networks 75 J.-S. Lin Automatic Training of Min-Max Classifiers 101 A. Rizzi Granular Computing in Pattern Recognition 125 W. Pedrycz and G. Vukovich ART-Based Model Set for Pattern Recognition: FasArt Family 145 G. I. Sainz Palmero et al. APPLICATIONS 177 A Methodology and a System for Adaptive Speech Recognition in a Noisy Environment Based on Adaptive Noise Cancellation and Evolving Fuzzy Neural Networks 179 N. Kasabov and G. Iliev Neural Versus Heuristic Development of Choquet Fuzzy Integral Fusion Algorithms for Land Mine Detection 205 P. D. Gader et al Automatic Segmentation of Multi-Spectral MR Brain Images Using a Neuro-Fuzzy Algorithm 227 S. Y. Lee et al. Vision-Based Neuro-Fuzzy Control of Autonomous Lane Following Vehicle 249 Y.-J. Ryoo. 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. 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.
