research-article

Parameters of GFSSAM: coding the parameters of a hybrid genetic fuzzy system

Author:

Penka GeorgievaAuthors Info & Claims

ICTRS '18: Proceedings of the Seventh International Conference on Telecommunications and Remote Sensing

Pages 85 - 92

https://doi.org/10.1145/3278161.3278174

Published: 08 October 2018 Publication History

Abstract

Context-driven systems provide information and/or services to the user and have specific features: a vaguely defined range of variables, contextual model, and context-sensitive services.

This paper is focused on coding the parameters of a hybrid genetic fuzzy system that is designed to use a genetic algorithm for optimizing the knowledge base of a previously created fuzzy system. The hybrid context-driven rule-based system (GFSSAM=Genetic Fuzzy Software System for Asset Management) is a real-time software system for supporting the decision process in managing investment assets. In the investment process stakeholders analyse, model, use raw data, and make decisions in which the perspective context plays a key role and so any software system in this area has to provide reliable context-aware results. GFSSAM is developed and tested on real-time data from the stock exchange in a project for extensive research on optimization of the inference machine and knowledge base of a fuzzy system through hybridization.

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Cited By

Popchev I(2021)Soft Computing: Three Decades Fuzzy Models and ApplicationsResearch in Computer Science in the Bulgarian Academy of Sciences10.1007/978-3-030-72284-5_5(55-100)Online publication date: 20-Jul-2021
https://doi.org/10.1007/978-3-030-72284-5_5
Simionov RMollova SDolchinkov R(2020)Integrated laboratory complex2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO)10.23919/MIPRO48935.2020.9245339(1567-1572)Online publication date: 28-Sep-2020
https://doi.org/10.23919/MIPRO48935.2020.9245339
Zaerov ELetskovska SSeymenliyski KSimionov R(2020)PV System Monitoring by IoT in Smart UniversityProceedings of the 9th International Conference on Telecommunications and Remote Sensing10.1145/3430116.3430124(44-49)Online publication date: 5-Oct-2020
https://dl.acm.org/doi/10.1145/3430116.3430124
Show More Cited By

Index Terms

Parameters of GFSSAM: coding the parameters of a hybrid genetic fuzzy system
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Vagueness and fuzzy logic
  2. Machine learning
    1. Machine learning approaches
      1. Bio-inspired approaches
        Genetic algorithms

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cover image ACM Other conferences

ICTRS '18: Proceedings of the Seventh International Conference on Telecommunications and Remote Sensing

October 2018

91 pages

ISBN:9781450365802

DOI:10.1145/3278161

General Chairs:
Marijn Janssen
TU Delft - Delft University of Technology (The Netherlands)
,
Boris Shishkov
Institute of Mathematics and Informatics - Bulgarian Academy of Sciences/University of Library Studies and Information Technology/IICREST (Bulgaria)
,
Program Chairs:
Andon Lazarov
BFU - Burgas Free University (Bulgaria)/K.N. Toosi University of Technology (Iran)
,
Dimitris Mitrakos
AUTH - Aristotle University of Thessaloniki (Greece)

Copyright © 2018 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 October 2018

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ICTRS '18

ICTRS '18: 7th International Conference on Telecommunications and Remote Sensing

October 8 - 9, 2018

Barcelona, Spain

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Overall Acceptance Rate 7 of 15 submissions, 47%

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Cited By

Popchev I(2021)Soft Computing: Three Decades Fuzzy Models and ApplicationsResearch in Computer Science in the Bulgarian Academy of Sciences10.1007/978-3-030-72284-5_5(55-100)Online publication date: 20-Jul-2021
https://doi.org/10.1007/978-3-030-72284-5_5
Simionov RMollova SDolchinkov R(2020)Integrated laboratory complex2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO)10.23919/MIPRO48935.2020.9245339(1567-1572)Online publication date: 28-Sep-2020
https://doi.org/10.23919/MIPRO48935.2020.9245339
Zaerov ELetskovska SSeymenliyski KSimionov R(2020)PV System Monitoring by IoT in Smart UniversityProceedings of the 9th International Conference on Telecommunications and Remote Sensing10.1145/3430116.3430124(44-49)Online publication date: 5-Oct-2020
https://dl.acm.org/doi/10.1145/3430116.3430124
Georgieva P(2020)Fuzzy System for Building Energy Flows ManagementIntelligent and Fuzzy Techniques: Smart and Innovative Solutions10.1007/978-3-030-51156-2_142(1216-1224)Online publication date: 11-Jul-2020
https://doi.org/10.1007/978-3-030-51156-2_142
Georgieva PDolchinkov R(2019)Fuzzy Models for Managing a Micro Grid PV System2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)10.23919/MIPRO.2019.8756654(1066-1071)Online publication date: May-2019
https://doi.org/10.23919/MIPRO.2019.8756654

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