A brain-inspired fuzzy semantic memory model for learning and reasoning with uncertainty
Abstract Decision-making pervades the human experience. The human decision process is driven by rational reasoning, which is the capacity to use the faculty of reason to facilitate logical thinking and to derive uncertain but sensible arguments from existing knowledge and the observed fact. Knowledg...
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| Autor*in: |
Tung, W. L. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2007 |
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| Schlagwörter: |
Brain-inspired learning memory model |
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| Anmerkung: |
© Springer-Verlag London Limited 2007 |
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| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - Springer-Verlag, 1993, 16(2007), 6 vom: 11. Apr., Seite 559-569 |
|---|---|
| Übergeordnetes Werk: |
volume:16 ; year:2007 ; number:6 ; day:11 ; month:04 ; pages:559-569 |
| Links: |
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| DOI / URN: |
10.1007/s00521-007-0101-2 |
|---|
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OLC2025580525 |
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| 520 | |a Abstract Decision-making pervades the human experience. The human decision process is driven by rational reasoning, which is the capacity to use the faculty of reason to facilitate logical thinking and to derive uncertain but sensible arguments from existing knowledge and the observed fact. Knowledge refers to the accumulation and the continuous neurological organization of information via the repeated exposure to its effective usage. Functionally, a decision support system seeks to provide a systematic and human-like way to data analysis by synthesizing an expert’s knowledge and reasoning capability to support the decision process of the user. However, conventional knowledge engineering and decision support systems often performed poorly when they are applied to problem domains festered with uncertain information, where the quality of the observed data is compromised by measurement noises. This paper presents T2-GenSoFNN, a brain-inspired fuzzy semantic memory model embedded with Type-2 fuzzy logic inference for learning and reasoning with noise-corrupted data. The proposed T2-GenSoFNN model is applied to the modeling of human insulin levels for the proper regulation of blood glucose concentration in diabetes therapy. The results are encouraging. | ||
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10.1007/s00521-007-0101-2 doi (DE-627)OLC2025580525 (DE-He213)s00521-007-0101-2-p DE-627 ger DE-627 rakwb eng 004 VZ Tung, W. L. verfasserin aut A brain-inspired fuzzy semantic memory model for learning and reasoning with uncertainty 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2007 Abstract Decision-making pervades the human experience. The human decision process is driven by rational reasoning, which is the capacity to use the faculty of reason to facilitate logical thinking and to derive uncertain but sensible arguments from existing knowledge and the observed fact. Knowledge refers to the accumulation and the continuous neurological organization of information via the repeated exposure to its effective usage. Functionally, a decision support system seeks to provide a systematic and human-like way to data analysis by synthesizing an expert’s knowledge and reasoning capability to support the decision process of the user. However, conventional knowledge engineering and decision support systems often performed poorly when they are applied to problem domains festered with uncertain information, where the quality of the observed data is compromised by measurement noises. This paper presents T2-GenSoFNN, a brain-inspired fuzzy semantic memory model embedded with Type-2 fuzzy logic inference for learning and reasoning with noise-corrupted data. The proposed T2-GenSoFNN model is applied to the modeling of human insulin levels for the proper regulation of blood glucose concentration in diabetes therapy. The results are encouraging. GenSoFNN Brain-inspired learning memory model Discrete incremental clustering Type-2 fuzzy inference Insulin modeling Noise/uncertainty handling Quek, C. aut Enthalten in Neural computing & applications Springer-Verlag, 1993 16(2007), 6 vom: 11. Apr., Seite 559-569 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:16 year:2007 number:6 day:11 month:04 pages:559-569 https://doi.org/10.1007/s00521-007-0101-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_100 GBV_ILN_152 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_4046 GBV_ILN_4277 AR 16 2007 6 11 04 559-569 |
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10.1007/s00521-007-0101-2 doi (DE-627)OLC2025580525 (DE-He213)s00521-007-0101-2-p DE-627 ger DE-627 rakwb eng 004 VZ Tung, W. L. verfasserin aut A brain-inspired fuzzy semantic memory model for learning and reasoning with uncertainty 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2007 Abstract Decision-making pervades the human experience. The human decision process is driven by rational reasoning, which is the capacity to use the faculty of reason to facilitate logical thinking and to derive uncertain but sensible arguments from existing knowledge and the observed fact. Knowledge refers to the accumulation and the continuous neurological organization of information via the repeated exposure to its effective usage. Functionally, a decision support system seeks to provide a systematic and human-like way to data analysis by synthesizing an expert’s knowledge and reasoning capability to support the decision process of the user. However, conventional knowledge engineering and decision support systems often performed poorly when they are applied to problem domains festered with uncertain information, where the quality of the observed data is compromised by measurement noises. This paper presents T2-GenSoFNN, a brain-inspired fuzzy semantic memory model embedded with Type-2 fuzzy logic inference for learning and reasoning with noise-corrupted data. The proposed T2-GenSoFNN model is applied to the modeling of human insulin levels for the proper regulation of blood glucose concentration in diabetes therapy. The results are encouraging. GenSoFNN Brain-inspired learning memory model Discrete incremental clustering Type-2 fuzzy inference Insulin modeling Noise/uncertainty handling Quek, C. aut Enthalten in Neural computing & applications Springer-Verlag, 1993 16(2007), 6 vom: 11. Apr., Seite 559-569 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:16 year:2007 number:6 day:11 month:04 pages:559-569 https://doi.org/10.1007/s00521-007-0101-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_100 GBV_ILN_152 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_4046 GBV_ILN_4277 AR 16 2007 6 11 04 559-569 |
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10.1007/s00521-007-0101-2 doi (DE-627)OLC2025580525 (DE-He213)s00521-007-0101-2-p DE-627 ger DE-627 rakwb eng 004 VZ Tung, W. L. verfasserin aut A brain-inspired fuzzy semantic memory model for learning and reasoning with uncertainty 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2007 Abstract Decision-making pervades the human experience. The human decision process is driven by rational reasoning, which is the capacity to use the faculty of reason to facilitate logical thinking and to derive uncertain but sensible arguments from existing knowledge and the observed fact. Knowledge refers to the accumulation and the continuous neurological organization of information via the repeated exposure to its effective usage. Functionally, a decision support system seeks to provide a systematic and human-like way to data analysis by synthesizing an expert’s knowledge and reasoning capability to support the decision process of the user. However, conventional knowledge engineering and decision support systems often performed poorly when they are applied to problem domains festered with uncertain information, where the quality of the observed data is compromised by measurement noises. This paper presents T2-GenSoFNN, a brain-inspired fuzzy semantic memory model embedded with Type-2 fuzzy logic inference for learning and reasoning with noise-corrupted data. The proposed T2-GenSoFNN model is applied to the modeling of human insulin levels for the proper regulation of blood glucose concentration in diabetes therapy. The results are encouraging. GenSoFNN Brain-inspired learning memory model Discrete incremental clustering Type-2 fuzzy inference Insulin modeling Noise/uncertainty handling Quek, C. aut Enthalten in Neural computing & applications Springer-Verlag, 1993 16(2007), 6 vom: 11. Apr., Seite 559-569 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:16 year:2007 number:6 day:11 month:04 pages:559-569 https://doi.org/10.1007/s00521-007-0101-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_100 GBV_ILN_152 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_4046 GBV_ILN_4277 AR 16 2007 6 11 04 559-569 |
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10.1007/s00521-007-0101-2 doi (DE-627)OLC2025580525 (DE-He213)s00521-007-0101-2-p DE-627 ger DE-627 rakwb eng 004 VZ Tung, W. L. verfasserin aut A brain-inspired fuzzy semantic memory model for learning and reasoning with uncertainty 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2007 Abstract Decision-making pervades the human experience. The human decision process is driven by rational reasoning, which is the capacity to use the faculty of reason to facilitate logical thinking and to derive uncertain but sensible arguments from existing knowledge and the observed fact. Knowledge refers to the accumulation and the continuous neurological organization of information via the repeated exposure to its effective usage. Functionally, a decision support system seeks to provide a systematic and human-like way to data analysis by synthesizing an expert’s knowledge and reasoning capability to support the decision process of the user. However, conventional knowledge engineering and decision support systems often performed poorly when they are applied to problem domains festered with uncertain information, where the quality of the observed data is compromised by measurement noises. This paper presents T2-GenSoFNN, a brain-inspired fuzzy semantic memory model embedded with Type-2 fuzzy logic inference for learning and reasoning with noise-corrupted data. The proposed T2-GenSoFNN model is applied to the modeling of human insulin levels for the proper regulation of blood glucose concentration in diabetes therapy. The results are encouraging. GenSoFNN Brain-inspired learning memory model Discrete incremental clustering Type-2 fuzzy inference Insulin modeling Noise/uncertainty handling Quek, C. aut Enthalten in Neural computing & applications Springer-Verlag, 1993 16(2007), 6 vom: 11. Apr., Seite 559-569 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:16 year:2007 number:6 day:11 month:04 pages:559-569 https://doi.org/10.1007/s00521-007-0101-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_100 GBV_ILN_152 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_4046 GBV_ILN_4277 AR 16 2007 6 11 04 559-569 |
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10.1007/s00521-007-0101-2 doi (DE-627)OLC2025580525 (DE-He213)s00521-007-0101-2-p DE-627 ger DE-627 rakwb eng 004 VZ Tung, W. L. verfasserin aut A brain-inspired fuzzy semantic memory model for learning and reasoning with uncertainty 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2007 Abstract Decision-making pervades the human experience. The human decision process is driven by rational reasoning, which is the capacity to use the faculty of reason to facilitate logical thinking and to derive uncertain but sensible arguments from existing knowledge and the observed fact. Knowledge refers to the accumulation and the continuous neurological organization of information via the repeated exposure to its effective usage. Functionally, a decision support system seeks to provide a systematic and human-like way to data analysis by synthesizing an expert’s knowledge and reasoning capability to support the decision process of the user. However, conventional knowledge engineering and decision support systems often performed poorly when they are applied to problem domains festered with uncertain information, where the quality of the observed data is compromised by measurement noises. This paper presents T2-GenSoFNN, a brain-inspired fuzzy semantic memory model embedded with Type-2 fuzzy logic inference for learning and reasoning with noise-corrupted data. The proposed T2-GenSoFNN model is applied to the modeling of human insulin levels for the proper regulation of blood glucose concentration in diabetes therapy. The results are encouraging. GenSoFNN Brain-inspired learning memory model Discrete incremental clustering Type-2 fuzzy inference Insulin modeling Noise/uncertainty handling Quek, C. aut Enthalten in Neural computing & applications Springer-Verlag, 1993 16(2007), 6 vom: 11. Apr., Seite 559-569 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:16 year:2007 number:6 day:11 month:04 pages:559-569 https://doi.org/10.1007/s00521-007-0101-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_100 GBV_ILN_152 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_4046 GBV_ILN_4277 AR 16 2007 6 11 04 559-569 |
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A brain-inspired fuzzy semantic memory model for learning and reasoning with uncertainty |
| abstract |
Abstract Decision-making pervades the human experience. The human decision process is driven by rational reasoning, which is the capacity to use the faculty of reason to facilitate logical thinking and to derive uncertain but sensible arguments from existing knowledge and the observed fact. Knowledge refers to the accumulation and the continuous neurological organization of information via the repeated exposure to its effective usage. Functionally, a decision support system seeks to provide a systematic and human-like way to data analysis by synthesizing an expert’s knowledge and reasoning capability to support the decision process of the user. However, conventional knowledge engineering and decision support systems often performed poorly when they are applied to problem domains festered with uncertain information, where the quality of the observed data is compromised by measurement noises. This paper presents T2-GenSoFNN, a brain-inspired fuzzy semantic memory model embedded with Type-2 fuzzy logic inference for learning and reasoning with noise-corrupted data. The proposed T2-GenSoFNN model is applied to the modeling of human insulin levels for the proper regulation of blood glucose concentration in diabetes therapy. The results are encouraging. © Springer-Verlag London Limited 2007 |
| abstractGer |
Abstract Decision-making pervades the human experience. The human decision process is driven by rational reasoning, which is the capacity to use the faculty of reason to facilitate logical thinking and to derive uncertain but sensible arguments from existing knowledge and the observed fact. Knowledge refers to the accumulation and the continuous neurological organization of information via the repeated exposure to its effective usage. Functionally, a decision support system seeks to provide a systematic and human-like way to data analysis by synthesizing an expert’s knowledge and reasoning capability to support the decision process of the user. However, conventional knowledge engineering and decision support systems often performed poorly when they are applied to problem domains festered with uncertain information, where the quality of the observed data is compromised by measurement noises. This paper presents T2-GenSoFNN, a brain-inspired fuzzy semantic memory model embedded with Type-2 fuzzy logic inference for learning and reasoning with noise-corrupted data. The proposed T2-GenSoFNN model is applied to the modeling of human insulin levels for the proper regulation of blood glucose concentration in diabetes therapy. The results are encouraging. © Springer-Verlag London Limited 2007 |
| abstract_unstemmed |
Abstract Decision-making pervades the human experience. The human decision process is driven by rational reasoning, which is the capacity to use the faculty of reason to facilitate logical thinking and to derive uncertain but sensible arguments from existing knowledge and the observed fact. Knowledge refers to the accumulation and the continuous neurological organization of information via the repeated exposure to its effective usage. Functionally, a decision support system seeks to provide a systematic and human-like way to data analysis by synthesizing an expert’s knowledge and reasoning capability to support the decision process of the user. However, conventional knowledge engineering and decision support systems often performed poorly when they are applied to problem domains festered with uncertain information, where the quality of the observed data is compromised by measurement noises. This paper presents T2-GenSoFNN, a brain-inspired fuzzy semantic memory model embedded with Type-2 fuzzy logic inference for learning and reasoning with noise-corrupted data. The proposed T2-GenSoFNN model is applied to the modeling of human insulin levels for the proper regulation of blood glucose concentration in diabetes therapy. The results are encouraging. © Springer-Verlag London Limited 2007 |
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| container_issue |
6 |
| title_short |
A brain-inspired fuzzy semantic memory model for learning and reasoning with uncertainty |
| url |
https://doi.org/10.1007/s00521-007-0101-2 |
| remote_bool |
false |
| author2 |
Quek, C. |
| author2Str |
Quek, C. |
| ppnlink |
165669608 |
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| doi_str |
10.1007/s00521-007-0101-2 |
| up_date |
2024-07-04T01:36:19.040Z |
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7.3990183 |