This book presents machine learning and type-2 fuzzy sets for the prediction of time-series with a particular focus on business forecasting applications. It also proposes new uncertainty management techniques in an economic time-series using type-2 fuzzy sets for prediction of the time-series at a given time point from its preceding value in fluctuating business environments. It employs machine learning to determine repetitively occurring similar structural patterns in the time-series and uses stochastic automaton to predict the most probabilistic structure at a given partition of the time-series. Such predictions help in determining probabilistic moves in a stock index time-series
Primarily written for graduate students and researchers in computer science, the book is equally useful for researchers/professionals in business intelligence and stock index prediction. A background of undergraduate level mathematics is presumed, although not mandatory, for most of the sections. Exercises with tips are provided at the end of each chapter to the readers' ability and understanding of the topics covered.
Rapid advances in technology require materials with improved property profiles. Polymer modification using grafting and crosslinking are key ways to achieve this in an economical way and without the need for developing new materials. Often widely disparate and in a number of references, practical information on polymer grafting and crosslinking is now available in one volume. Researchers seeking information that bridges the knowledge gap between the scientific principles and industrial applications of polymer crosslinking and grafting will find coverage on the basic science, the methodologies, and a focus on the specific techniques used in a variety of industrial applications such as automotive, laminates, paints, adhesives, and cable. Coverage also includes potential biomedical applications. Descriptions of analytical tools that can be used to evaluate the results are also included.