2016 - IEEE Fellow For contributions to neuro-fuzzy and autonomous learning systems
Plamen Angelov mostly deals with Artificial intelligence, Data mining, Machine learning, Fuzzy logic and Cluster analysis. Plamen Angelov interconnects Identification, Data science and Pattern recognition in the investigation of issues within Artificial intelligence. His research in Data mining intersects with topics in Theoretical computer science, Classifier, Fuzzy rule, Outlier and Data stream.
His study in the fields of Activity recognition under the domain of Machine learning overlaps with other disciplines such as Streaming data. His research investigates the connection between Fuzzy logic and topics such as Artificial neural network that intersect with problems in Feature. His research integrates issues of Algorithm, Data stream mining, Analytics and Data analysis in his study of Cluster analysis.
His main research concerns Artificial intelligence, Machine learning, Fuzzy logic, Data mining and Fuzzy rule. Artificial intelligence is closely attributed to Pattern recognition in his research. The concepts of his Machine learning study are interwoven with issues in Contextual image classification, Structure and Identification.
Within one scientific family, Plamen Angelov focuses on topics pertaining to Control theory under Fuzzy logic, and may sometimes address concerns connected to Process control. His Data mining research focuses on Cluster analysis and how it relates to Algorithm. His research investigates the connection between Fuzzy set operations and topics such as Neuro-fuzzy that intersect with issues in Fuzzy classification, Fuzzy number and Defuzzification.
Plamen Angelov focuses on Artificial intelligence, Machine learning, Classifier, Fuzzy logic and Data mining. His research on Artificial intelligence often connects related areas such as Pattern recognition. His Machine learning research incorporates elements of Teamwork and Domain.
His studies examine the connections between Classifier and genetics, as well as such issues in Rule-based system, with regards to Feature descriptor, Remote sensing and Set. His Fuzzy logic research is multidisciplinary, incorporating elements of Massively parallel, Convergence, Control theory, Ranking and Rule based classifier. His studies deal with areas such as Computational intelligence and Cluster analysis as well as Data mining.
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Classifier, Cluster analysis and Algorithm. His Artificial intelligence research includes themes of Analytics and Pattern recognition. The study incorporates disciplines such as Image processing, Probabilistic logic, Class and Data space in addition to Machine learning.
His biological study spans a wide range of topics, including Object detection, Fuzzy rule, Fuzzy logic and Rule-based system. His study looks at the relationship between Cluster analysis and topics such as Data mining, which overlap with Evolving intelligent system. His Algorithm research integrates issues from Nonparametric statistics, Cosine similarity, Metric and Benchmark.
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An approach to online identification of Takagi-Sugeno fuzzy models
P.P. Angelov;D.P. Filev.
systems man and cybernetics (2004)
Evolving Fuzzy-Rule-Based Classifiers From Data Streams
P.P. Angelov;Xiaowei Zhou.
IEEE Transactions on Fuzzy Systems (2008)
Evolving Intelligent Systems: Methodology and Applications
Plamen Angelov;Dimitar P. Filev;Nik Kasabov.
(2010)
Evolving Rule-Based Models: A Tool for Design of Flexible Adaptive Systems
Plamen P. Angelov.
(2002)
Optimization in an intuitionistic fuzzy environment
Plamen P. Angelov.
Fuzzy Sets and Systems (1997)
Evolving Fuzzy Systems from Data Streams in Real-Time
P. Angelov;Xiaowei Zhou.
2006 International Symposium on Evolving Fuzzy Systems (2006)
Simpl_eTS: a simplified method for learning evolving Takagi-Sugeno fuzzy models
P. Angelov;D. Filev.
ieee international conference on fuzzy systems (2005)
Evolving Fuzzy Systems.
Plamen P. Angelov.
Encyclopedia of Complexity and Systems Science (2009)
PANFIS: A Novel Incremental Learning Machine
Mahardhika Pratama;Sreenatha G. Anavatti;Plamen P. Angelov;Edwin Lughofer.
IEEE Transactions on Neural Networks (2014)
Evolving Rule-Based Models
Plamen P. Angelov.
(2002)
Evolving Systems
(Impact Factor: 2.347)
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