Evolving Intelligent Systems / IEEE Press Series on Computational Intelligence (PDF)

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.