EAIS 2013 - EAIS 2013 2013 IEEE Symposium on Evolving and Adaptive Intelligent Systems

EAIS 2013

2013 IEEE Symposium on Evolving and Adaptive Intelligent Systems

The true intelligent systems should be dynamically evolving and be able to adapt and learn. The concept of evolving intelligent systems was established recently as a synergy between conventional systems, neural networks and fuzzy systems as structures for information representation and real time methods for machine learning. This emerging area targets non-stationary processes by developing novel on-line learning methods and computationally efficient algorithms for real-time applications. One of the important research challenges today is to develop methodologies, concepts, algorithms and techniques towards the design of intelligent systems with a higher level of flexibility and autonomy, so that the systems can evolve their structure and knowledge of the environment and ultimately - evolve their intelligence. That is, the system must be able to evolve, to self-develop, to self-organize, to self-evaluate and to self-improve. Wireless sensor networks, assisted ambient intelligence, embedded soft computing diagnostics and prognostics algorithms, intelligent agents, smart evolving sensors; autonomous robotic systems etc. are some of the natural implementation areas of evolving and adaptive intelligent systems. EAIS'13 continues the tradition set by the previous forums (EFS'06, GEFS'08, ESDIS'09, EIS'10) and is supported and organised by the Adaptive and Evolving Fuzzy Systems (AEFS) Task Force, FSTC, CIS, IEEE.

Topics

New Adaptive and Evolving Learning Methods
Stability, Robustness, Unlearning Effects
Structure Flexibility and Robustness in Evolving Systems
Evolving in Dynamic Environments
Drift and Shift in Data Streams
Self-monitoring Evolving Systems
Evolving Decision Systems
Evolving Perceptions
Self-organising Systems
Neural Networks with Evolving Structure
Non-stationary Time Series Prediction with Evolving Systems
Automatic Novelty Detection in Evolving Systems
On-Line Identification of Fuzzy Systems
Evolving Neuro-fuzzy Systems
Evolving Fuzzy Clustering Methods
Evolving Fuzzy Rule-based Classifiers
Evolving Regression-based Classifiers
Evolving Intelligent Systems for Time Series Prediction
Evolving Intelligent System State Monitoring and Prognostics
Methods
Evolving Intelligent Controllers
Evolving Fuzzy Decision Support Systems
Evolving Consumer Behaviour Models
Real-world application
Robotics
Control Systems
Industrial Applications
Data Mining and Knowledge Discovery
Intelligent Transport
Bio-Informatics
Defence
Keynote, Tutorial and Panel Sessions

Please forward your proposals with detailed abstract and bio-sketches of the speakers to Symposium Co-Chairs and SSCI Keynote-Tutorial Chair, Dr S Das.

Special Sessions

Please forward your special session proposals to Symposium Co-Chairs.

Symposium Co-Chairs

Plamen Angelov, Lancaster University, UK
Dimitar Filev, Ford, USA
Nikola Kasabov, Aukland University of Technology, New Zealand

Program Committee (tentative)

Adel Alimi
Plamen Angelov (Chair)
Jose Rubio Avila
Abdelhamid Bouchachia
Richard Duro
Panagiotis Chountas
Damien Coyle
Dimitar Filev (co-Chair)
Mario Gongora
Fernando Gomide
Antonio Medina Hernandez
Jose Iglesias
Janusz Kacprzyk
Petr Kadlec
Ilhem Kallel
Nik Kasabov (co-Chair)
Vitaliy Kolodyazhniy
Andre Lemos
Zsofia Lendek
Edwin Lughofer
Witold Pedrycz
Fernando Pouzols
Ignacio Rojas
Joao Sousa
Gancho Vachkov
Ronald Yager
Xiaojun Zeng