From theory to techniques, the first all-in-one resource for EIS

There is a clear demand in advanced process industries, defense, and Internet and communication (VoIP) applications for intelligent yet adaptive/evolving systems. Evolving Intelligent Systems is the first self- contained volume that covers this newly established concept in its entirety, from a systematic methodology to case studies to industrial applications. Featuring chapters written by leading world experts, it addresses the progress, trends, and major achievements in this emerging research field, with a strong emphasis on the balance between novel theoretical results and solutions and practical real-life applications.

• Explains the following fundamental approaches for developing evolving intelligent systems (EIS):

• • the Hierarchical Prioritized Structure

  • the Participatory Learning Paradigm

  • the Evolving Takagi-Sugeno fuzzy systems (eTS+)

  • the evolving clustering algorithm that stems from the well-known Gustafson-Kessel offline clustering algorithm

• Emphasizes the importance and increased interest in online processing of data streams

• Outlines the general strategy of using the fuzzy dynamic clustering as a foundation for evolvable information granulation

• Presents a methodology for developing robust and interpretable evolving fuzzy rule-based systems

• Introduces an integrated approach to incremental (real-time) feature extraction and classification

• Proposes a study on the stability of evolving neuro-fuzzy recurrent networks

• Details methodologies for evolving clustering and classification

• Reveals different applications of EIS to address real problems in areas of:

• • evolving inferential sensors in chemical and petrochemical industry

  • learning and recognition in robotics

• Features downloadable software resources

Evolving Intelligent Systems is the one-stop reference guide for both theoretical and practical issues for computer scientists, engineers, researchers, applied mathematicians, machine learning and data mining experts, graduate students, and professionals.