@inproceedings{d3389f6246fa4fb7ad40f9ea1442537c,
title = "Self-learning data processing framework based on computational intelligence enhancing autonomous control by machine intelligence",
abstract = "A generic framework for evolving and autonomously controlled systems has been developed and evaluated in this paper. A three-phase approach aimed at identification, classification of anomalous data and at prediction of its consequences is applied to processing sensory inputs from multiple data sources. An ad-hoc activation of sensors and processing of data minimises the quantity of data that needs to be analysed at any one time. Adaptability and autonomy are achieved through the combined use of statistical analysis, computational intelligence and clustering techniques. A genetic algorithm is used to optimise the choice of data sources, the type and characteristics of the analysis undertaken. The experimental results have demonstrated that the framework is generally applicable to various problem domains and reasonable performance is achieved in terms of computational intelligence accuracy rate. Online learning can also be used to dynamically adapt the system in near real time.",
keywords = "Anomalies, Computational intelligence, Evolving and autonomous systems, Robot controls",
author = "Prapa Rattadilok and Andrei Petrovski",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, EALS 2014 ; Conference date: 09-12-2014 Through 12-12-2014",
year = "2014",
month = jan,
day = "13",
doi = "10.1109/EALS.2014.7009508",
language = "English",
series = "IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "87--94",
booktitle = "IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014",
address = "United States",
}
