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Evolving Intelligent Systems: Methodology and Applications (IEEE Press Series on Computational Intelligence)

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One of the consequences of the information revolution is the ever-growing amount of
information we are surrounded with and the need to process this information
efficiently and extract meaningful knowledge. This phenomenon was termed “digital
obesity” or “information obesity” by Toshiba Ltd. One of the specifics of this
phenomenon is that the information not only is presented in huge quantities, but
also is dynamic, presented in a form of streams of data. The latter brings new
challenges to the established approaches to processing data, usually in an offline and
stationary form. To address the new challenges of extracting highly interpretable
knowledge from streams of data, online techniques and methodologies have been
developed, so that a higher level of adaptation, compared to conventional adaptive
systems known from control theory (Astrom & Wittenmark, 1989), is achieved.
These new modeling methods are also principally different from the classical system
identification theory (Ljung, 1988). They differ also from the traditional machine
learning and statistical learning methods (Hastie et al., 2001), where the processes
are usually assumed to have Gaussian distribution and random nature. Evolving
intelligent systems (EISs) are based on fuzzy and neuro-fuzzy techniques that allow
for the structure and the functionality of a system to develop and evolve from
incoming data. They represent a fuzzy mixture of locally valid simpler systems,
which in combination are highly nonlinear and non-Gaussian. They can be considered
fuzzy mixtures of Gaussians, but these mixtures are not pre-fixed and are adapting/
evolving to capture the real data density distribution (Angelov & Zhou, 2008).

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.

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