1 Elementary Rough Set Granules: Toward a Rough Set Processor.- 2 Rough-Neural Computing: An Introduction.- 3 Information Granules and Rough-Neural Computing.- 4 A Rough-Neural Computation Model Based on Rough Mereology.- 5 Knowledge-Based Networking in Granular Worlds.- 6 Adaptive Aspects of Combining Approximation Spaces.- 7 Algebras from Rough Sets.- 8 Approximation Transducers and Trees: A Technique for Combining Rough and Crisp Knowledge.- 9 Using Contextually Closed Queries for Local Closed-World Reasoning in Rough Knowledge Databases.- 10 On Model Evaluation, Indexes of Importance, and Interaction Values in Rough Set Analysis.- 11 New Fuzzy Rough Sets Based on Certainty Qualification.- 12 Toward Rough Datalog: Embedding Rough Sets in Prolog.- 13 On Exploring Soft Discretization of Continuous Attributes.- 14 Rough-SOM with Fuzzy Discretization.- 15 Biomedical Inference: A Semantic Model.- 16 Fundamental Mathematical Notions of the Theory of Socially Embedded Games: A Granular Computing Perspective.- 17 Fuzzy Games and Equilibria: The Perspective of the General Theory of Games on Nash and Normative Equilibria.- 18 Rough Neurons: Petri Net Models and Applications.- 19 Information Granulation in a Decision-Theoretical Model of Rough Sets.- 20 Intelligent Acquisition of Audio Signals Employing Neural Networks and Rough Set Algorithms.- 21 An Approach to Imbalanced Data Sets Based on Changing Rule Strength.- 22 Rough-Neural Approach to Testing the Influence of Visual Cues on Surround Sound Perception.- 23 Handwritten Digit Recognition Using Adaptive Classifier Construction Techniques.- 24 From Rough through Fuzzy to Crisp Concepts: Case Study on Image Color Temperature Description.- 25 Information Granulation and Pattern Recognition.- 26 Computational Analysis of Acquired Dyslexia of Kanji Characters Based on Conventional and Rough Neural Networks.- 27 WaRS: A Method for Signal Classification.- 28 A Hybrid Model for Rule Discovery in Data.- Author Index.

Soft computing comprises various paradigms dedicated to approximately solving real-world problems, e.g., in decision making, classification or learning; among these paradigms are fuzzy sets, rough sets, neural networks, and genetic algorithms. It is well understood now in the soft computing community that hybrid approaches combining various paradigms provide very promising attempts to solving complex problems. Exploiting the potential and strength of both neural networks and rough sets, this book is devoted to rough-neurocomputing which is also related to the novel aspect of computing based on information granulation, in particular to computing with words. It provides foundational and methodological issues as well as applications in various fields.

From the reviews: "This book (actually edited volume) presents recent advances in the area of rough neural computer from a wide konge (sic) of perspectives. … The book contains a few introductory articles suitable to understand what the field of rough several computing is about." (Jan Friso Groote, Zentralblatt MATH, Vol. 1040(9), 2004)