edited by H Bunke (University of Bern, Switzerland) & A Kandel (University of South Florida, USA)

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.

• Methodology:
  • Simultaneous Feature Analysis and System Identification in a Neuro-Fuzzy Framework (N R Pal & 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 & 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 & 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)