Several statistical techniques are used for the design of materials through extraction of knowledge from existing data banks. These approaches are getting more attention with the application of computational intelligence techniques. This book illustrates the alternative but effective methods of designing materials, where models are developed through capturing the inherent correlations among the variables on the basis of available imprecise knowledge in the form of rules or database, as well as through the extraction of knowledge from experimental or industrial database, and using optimization tools.
Table of Contents
Introduction. A brief overview of traditional approaches to materials design. Statistics and data mining concepts. Principles of neural network and other soft modeling techniques. Knowledge extraction using rough and fuzzy set theories. Handling imprecise knowledge through fuzzy inference system. Evolutionary algorithm for designing materials. Technique blends to suit a materials system. Hybridizing with traditional approaches. Designing microstructure. Concluding remarks.