Self-learning data processing framework based on computational intelligence enhancing autonomous control by machine intelligence

Prapa Rattadilok; Andrei Petrovski

doi:10.1109/EALS.2014.7009508

Self-learning data processing framework based on computational intelligence enhancing autonomous control by machine intelligence

Prapa Rattadilok
, Andrei Petrovski

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

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.

Original language	English
Title of host publication	IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014
Subtitle of host publication	2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	87-94
Number of pages	8
ISBN (Electronic)	9781479944958
DOIs	https://doi.org/10.1109/EALS.2014.7009508
Publication status	Published - 13 Jan 2014
Externally published	Yes
Event	2014 IEEE Symposium on Evolving and Autonomous Learning Systems, EALS 2014 - Orlando, United States Duration: 9 Dec 2014 → 12 Dec 2014

Publication series

Name	IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings

Conference

Conference	2014 IEEE Symposium on Evolving and Autonomous Learning Systems, EALS 2014
Country/Territory	United States
City	Orlando
Period	9/12/14 → 12/12/14

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 4 Quality Education

Free Keywords

Anomalies
Computational intelligence
Evolving and autonomous systems
Robot controls

ASJC Scopus subject areas

Artificial Intelligence

Access to Document

10.1109/EALS.2014.7009508

Cite this

Rattadilok, P., & Petrovski, A. (2014). Self-learning data processing framework based on computational intelligence enhancing autonomous control by machine intelligence. In IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings (pp. 87-94). Article 7009508 (IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EALS.2014.7009508

Rattadilok, Prapa ; Petrovski, Andrei. / Self-learning data processing framework based on computational intelligence enhancing autonomous control by machine intelligence. IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 87-94 (IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings).

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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",

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series = "IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings",

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Rattadilok, P & Petrovski, A 2014, Self-learning data processing framework based on computational intelligence enhancing autonomous control by machine intelligence. in IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings., 7009508, IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 87-94, 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, EALS 2014, Orlando, United States, 9/12/14. https://doi.org/10.1109/EALS.2014.7009508

Self-learning data processing framework based on computational intelligence enhancing autonomous control by machine intelligence. / Rattadilok, Prapa; Petrovski, Andrei.
IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. p. 87-94 7009508 (IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

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N2 - 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.

AB - 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.

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Rattadilok P, Petrovski A. Self-learning data processing framework based on computational intelligence enhancing autonomous control by machine intelligence. In IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2014. p. 87-94. 7009508. (IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings). doi: 10.1109/EALS.2014.7009508

Self-learning data processing framework based on computational intelligence enhancing autonomous control by machine intelligence

Abstract

Publication series

Conference

UN SDGs

Free Keywords

ASJC Scopus subject areas

Access to Document

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