Hybrid Granular Fuzzy-Neural Learners Engineering: Theory and Practice. Soft Computing Everywhere

      Аннотация: The purpose of the book is to illustrate the conception, the construction and use of hybrid granular Min-Max fuzzy-neural relational learners in a wide spectrum of practical applications ranging from handwriting recognition, vision engineering, medical diagnosis, to software quality prediction and understanding in software engineering. The aspects of verification and validation (V&V) are covered using a theory of generalization based on value approximation. The granular soft computing methods proposed and employed are sufficiently mature (in one case approaching international standards) that basic tutorial material is not included if readily accessible elsewhere. It is hoped that the reader will gain insight into the practice of hybrid granular soft computing methods as well as motivation for further study of their theoretical foundations. The potential for such a hybrid methodology holds promise in the software industry.

Ключевые слова: machine learning, pattern recognition, Image Processing, Validation and Verification, Soft Computing, Granular Computing, fuzzy relationnal calculus, software quality, software evolution, software preventive maintenance, medical diagnosis, Human Factors in Software Engineering, Vision Engineering