On-line adaptive clustering for process monitoring and fault detection
Expert Systems with Applications: An International Journal
Dynamic GP models: an overview and recent developments
ASM'12 Proceedings of the 6th international conference on Applied Mathematics, Simulation, Modelling
Thermal modeling of power transformers using evolving fuzzy systems
Engineering Applications of Artificial Intelligence
On employing fuzzy modeling algorithms for the valuation of residential premises
Information Sciences: an International Journal
Evolving fuzzy classifier based on the modified ECM algorithm for pattern classification
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Fuzzy machine learning and data mininga
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Information Sciences: an International Journal
Editorial: Editorial of the special issue: Online fuzzy machine learning and data mining
Information Sciences: an International Journal
Online extraction of main linear trends for nonlinear time-varying processes
Information Sciences: an International Journal
Evolving fuzzy pattern trees for binary classification on data streams
Information Sciences: an International Journal
Navigating interpretability issues in evolving fuzzy systems
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
A “learning from models” cognitive fault diagnosis system
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Learning deep belief networks from non-stationary streams
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Evolving Takagi-Sugeno fuzzy model based on switching to neighboring models
Applied Soft Computing
Exponential smoothing with credibility weighted observations
Information Sciences: an International Journal
Editorial: Special Issue: Evolving Soft Computing Techniques and Applications
Applied Soft Computing
Evolving intelligent algorithms for the modelling of brain and eye signals
Applied Soft Computing
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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.