A Competent Memetic Algorithm for Learning Fuzzy Cognitive Maps
Fuzzy cognitive maps (FCMs) form an important class of models for describing and simulating the behavior of dynamic systems through causal reasoning. Owing to their abilities to make the symbolic knowledge processing simple and transparent, FCMs have been successfully used to model the behavior of c...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Acampora, Giovanni [verfasserIn] |
|---|
| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: IEEE transactions on fuzzy systems - New York, NY : Inst., 1993, 23(2015), 6, Seite 2397-2411 |
|---|---|
| Übergeordnetes Werk: |
volume:23 ; year:2015 ; number:6 ; pages:2397-2411 |
| Links: |
|---|
| DOI / URN: |
10.1109/TFUZZ.2015.2426311 |
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OLC1959563939 |
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| 520 | |a Fuzzy cognitive maps (FCMs) form an important class of models for describing and simulating the behavior of dynamic systems through causal reasoning. Owing to their abilities to make the symbolic knowledge processing simple and transparent, FCMs have been successfully used to model the behavior of complex systems originating from numerous application areas, such as economy, politics, medicine, and engineering. However, the design of FCMs necessarily involves domain experts to develop a graph-based model composed of a collection of system's concepts and causal relationships among them. Consequently, since humans exhibit an intrinsic factor of subjectivity and are only able to efficiently develop small-size graph-based models, there is a legitimate need to devise methods capable of automatically learning FCM models from data. This research addresses this need by introducing a competent memetic algorithm to generate FCM models from available historical data, with no human intervention. Extensive benchmarking tests performed on both synthetic and real-world data quantify the performance of the competent memetic method and emphasize its suitability over the models obtained by conventional and noncompetent hybrid evolutionary approaches in terms of accuracy, approximation ability, and convergence speed. Moreover, the proposed approach is shown to be scalable due to its capability to efficiently learn high-dimensional FCM models. | ||
| 650 | 4 | |a Biological cells | |
| 650 | 4 | |a Memetic Algorithms | |
| 650 | 4 | |a Sociology | |
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| 650 | 4 | |a Algorithm design and analysis | |
| 650 | 4 | |a Memetics | |
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| 700 | 1 | |a Vitiello, Autilia |4 oth | |
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10.1109/TFUZZ.2015.2426311 doi PQ20160617 (DE-627)OLC1959563939 (DE-599)GBVOLC1959563939 (PRQ)c1888-25ccf9a0560e67b4e772c73e33575f714909283cb965509b27d4eed9927cd7650 (KEY)0226257620150000023000602397competentmemeticalgorithmforlearningfuzzycognitive DE-627 ger DE-627 rakwb eng 004 DNB Acampora, Giovanni verfasserin aut A Competent Memetic Algorithm for Learning Fuzzy Cognitive Maps 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Fuzzy cognitive maps (FCMs) form an important class of models for describing and simulating the behavior of dynamic systems through causal reasoning. Owing to their abilities to make the symbolic knowledge processing simple and transparent, FCMs have been successfully used to model the behavior of complex systems originating from numerous application areas, such as economy, politics, medicine, and engineering. However, the design of FCMs necessarily involves domain experts to develop a graph-based model composed of a collection of system's concepts and causal relationships among them. Consequently, since humans exhibit an intrinsic factor of subjectivity and are only able to efficiently develop small-size graph-based models, there is a legitimate need to devise methods capable of automatically learning FCM models from data. This research addresses this need by introducing a competent memetic algorithm to generate FCM models from available historical data, with no human intervention. Extensive benchmarking tests performed on both synthetic and real-world data quantify the performance of the competent memetic method and emphasize its suitability over the models obtained by conventional and noncompetent hybrid evolutionary approaches in terms of accuracy, approximation ability, and convergence speed. Moreover, the proposed approach is shown to be scalable due to its capability to efficiently learn high-dimensional FCM models. Biological cells Memetic Algorithms Sociology Statistics Biological system modeling Convergence Fuzzy Cognitive Maps Algorithm design and analysis Memetics Dynamic System Modelling Pedrycz, Witold oth Vitiello, Autilia oth Enthalten in IEEE transactions on fuzzy systems New York, NY : Inst., 1993 23(2015), 6, Seite 2397-2411 (DE-627)171085515 (DE-600)1149610-1 (DE-576)034198547 1063-6706 nnns volume:23 year:2015 number:6 pages:2397-2411 http://dx.doi.org/10.1109/TFUZZ.2015.2426311 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7094232 http://search.proquest.com/docview/1738862145 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_30 GBV_ILN_70 GBV_ILN_4318 AR 23 2015 6 2397-2411 |
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10.1109/TFUZZ.2015.2426311 doi PQ20160617 (DE-627)OLC1959563939 (DE-599)GBVOLC1959563939 (PRQ)c1888-25ccf9a0560e67b4e772c73e33575f714909283cb965509b27d4eed9927cd7650 (KEY)0226257620150000023000602397competentmemeticalgorithmforlearningfuzzycognitive DE-627 ger DE-627 rakwb eng 004 DNB Acampora, Giovanni verfasserin aut A Competent Memetic Algorithm for Learning Fuzzy Cognitive Maps 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Fuzzy cognitive maps (FCMs) form an important class of models for describing and simulating the behavior of dynamic systems through causal reasoning. Owing to their abilities to make the symbolic knowledge processing simple and transparent, FCMs have been successfully used to model the behavior of complex systems originating from numerous application areas, such as economy, politics, medicine, and engineering. However, the design of FCMs necessarily involves domain experts to develop a graph-based model composed of a collection of system's concepts and causal relationships among them. Consequently, since humans exhibit an intrinsic factor of subjectivity and are only able to efficiently develop small-size graph-based models, there is a legitimate need to devise methods capable of automatically learning FCM models from data. This research addresses this need by introducing a competent memetic algorithm to generate FCM models from available historical data, with no human intervention. Extensive benchmarking tests performed on both synthetic and real-world data quantify the performance of the competent memetic method and emphasize its suitability over the models obtained by conventional and noncompetent hybrid evolutionary approaches in terms of accuracy, approximation ability, and convergence speed. Moreover, the proposed approach is shown to be scalable due to its capability to efficiently learn high-dimensional FCM models. Biological cells Memetic Algorithms Sociology Statistics Biological system modeling Convergence Fuzzy Cognitive Maps Algorithm design and analysis Memetics Dynamic System Modelling Pedrycz, Witold oth Vitiello, Autilia oth Enthalten in IEEE transactions on fuzzy systems New York, NY : Inst., 1993 23(2015), 6, Seite 2397-2411 (DE-627)171085515 (DE-600)1149610-1 (DE-576)034198547 1063-6706 nnns volume:23 year:2015 number:6 pages:2397-2411 http://dx.doi.org/10.1109/TFUZZ.2015.2426311 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7094232 http://search.proquest.com/docview/1738862145 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_30 GBV_ILN_70 GBV_ILN_4318 AR 23 2015 6 2397-2411 |
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A Competent Memetic Algorithm for Learning Fuzzy Cognitive Maps |
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A Competent Memetic Algorithm for Learning Fuzzy Cognitive Maps |
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Acampora, Giovanni |
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IEEE transactions on fuzzy systems |
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10.1109/TFUZZ.2015.2426311 |
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competent memetic algorithm for learning fuzzy cognitive maps |
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A Competent Memetic Algorithm for Learning Fuzzy Cognitive Maps |
| abstract |
Fuzzy cognitive maps (FCMs) form an important class of models for describing and simulating the behavior of dynamic systems through causal reasoning. Owing to their abilities to make the symbolic knowledge processing simple and transparent, FCMs have been successfully used to model the behavior of complex systems originating from numerous application areas, such as economy, politics, medicine, and engineering. However, the design of FCMs necessarily involves domain experts to develop a graph-based model composed of a collection of system's concepts and causal relationships among them. Consequently, since humans exhibit an intrinsic factor of subjectivity and are only able to efficiently develop small-size graph-based models, there is a legitimate need to devise methods capable of automatically learning FCM models from data. This research addresses this need by introducing a competent memetic algorithm to generate FCM models from available historical data, with no human intervention. Extensive benchmarking tests performed on both synthetic and real-world data quantify the performance of the competent memetic method and emphasize its suitability over the models obtained by conventional and noncompetent hybrid evolutionary approaches in terms of accuracy, approximation ability, and convergence speed. Moreover, the proposed approach is shown to be scalable due to its capability to efficiently learn high-dimensional FCM models. |
| abstractGer |
Fuzzy cognitive maps (FCMs) form an important class of models for describing and simulating the behavior of dynamic systems through causal reasoning. Owing to their abilities to make the symbolic knowledge processing simple and transparent, FCMs have been successfully used to model the behavior of complex systems originating from numerous application areas, such as economy, politics, medicine, and engineering. However, the design of FCMs necessarily involves domain experts to develop a graph-based model composed of a collection of system's concepts and causal relationships among them. Consequently, since humans exhibit an intrinsic factor of subjectivity and are only able to efficiently develop small-size graph-based models, there is a legitimate need to devise methods capable of automatically learning FCM models from data. This research addresses this need by introducing a competent memetic algorithm to generate FCM models from available historical data, with no human intervention. Extensive benchmarking tests performed on both synthetic and real-world data quantify the performance of the competent memetic method and emphasize its suitability over the models obtained by conventional and noncompetent hybrid evolutionary approaches in terms of accuracy, approximation ability, and convergence speed. Moreover, the proposed approach is shown to be scalable due to its capability to efficiently learn high-dimensional FCM models. |
| abstract_unstemmed |
Fuzzy cognitive maps (FCMs) form an important class of models for describing and simulating the behavior of dynamic systems through causal reasoning. Owing to their abilities to make the symbolic knowledge processing simple and transparent, FCMs have been successfully used to model the behavior of complex systems originating from numerous application areas, such as economy, politics, medicine, and engineering. However, the design of FCMs necessarily involves domain experts to develop a graph-based model composed of a collection of system's concepts and causal relationships among them. Consequently, since humans exhibit an intrinsic factor of subjectivity and are only able to efficiently develop small-size graph-based models, there is a legitimate need to devise methods capable of automatically learning FCM models from data. This research addresses this need by introducing a competent memetic algorithm to generate FCM models from available historical data, with no human intervention. Extensive benchmarking tests performed on both synthetic and real-world data quantify the performance of the competent memetic method and emphasize its suitability over the models obtained by conventional and noncompetent hybrid evolutionary approaches in terms of accuracy, approximation ability, and convergence speed. Moreover, the proposed approach is shown to be scalable due to its capability to efficiently learn high-dimensional FCM models. |
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A Competent Memetic Algorithm for Learning Fuzzy Cognitive Maps |
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http://dx.doi.org/10.1109/TFUZZ.2015.2426311 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7094232 http://search.proquest.com/docview/1738862145 |
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Pedrycz, Witold Vitiello, Autilia |
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