Evolving Intelligent Systems: Methods, Algorithms and Applications

  • Andre Lemos
  • Walmir Caminhas
  • Fernando Gomide
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 13)


Evolving intelligent systems (EIS) are highly adaptive systems able to update its own parameters and structure based on a date stream. These systems have been developed to address problems of modeling, control, prediction, classification and data processing in a nonstationary, dynamic changing environment. Pioneers works in this area are dated from the around the turn of the centuries and were focused in areas of neural networks, fuzzy rule-based systems and neural-fuzzy hybrids. In this century the area has been expanded to also address statistical models, hardware implementations and so on. The aim of this chapter is to provide an introduction and a state of the art view about this subject. The purpose is to present the paradigm and the associated concepts, address the main learning approaches, and detail recently developed models based on participatory learning and fuzzy trees.


Membership Function Fuzzy Neural Network Load Forecast Arousal Index Consequent Parameter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Departament of Eletronic EngineeringUniversidade Federal de Minas GeraisPampulhaBrazil
  2. 2.Department of Computer Engineering and AutomationUniversity of CampinasCampinasBrazil

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