Autonomous Learning Systems: From Data Streams to Knowledge in Real-time

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Epilogue

This book was planned some five years ago as a vehicle to put in one place the theory and applications of the innovative research, but it evolved in parallel with the development and maturing of the investigations that led to some new and interesting results. The book is structured in three parts; Part I – Foundations, Part II – Methodology of ALS, and Part III – Applications of ALS.

Part I itself is composed of three chapters that represent the foundations of the main cognate areas of research, including probability theory, machine learning and pattern recognition, and fuzzy sets theory (including NFS). Although, this part of the book is rather introductory, new and original ideas and elements are presented, including the powerful (patent pending) concept of RDE, a new method for evolving clustering, ELM and the recent ground-breaking method for fuzzy and neurofuzzy systems modelling, AnYa.

Part II contains the theoretical basis of the innovative and powerful approach of ALS. In particular, Chapters 5 and 6 describe the principles and methodology for autonomous learning of system structure and parameters from data streams. Chapters 7–9 describe predictors, estimators, filters, autonomous learning sensors, classifiers and controllers using ALS. Chapter 10 describes the principles and procedures for collaborative ALS.

Finally, Part III consists of four chapters that describe various applications of ALS to areas as diverse as chemical and petrochemical industry, mobile ...