Natural Brain-Inspired Intelligence for Non-Gaussian and Nonlinear Environments with Finite Memory
The cyber processing layer of smart systems based on a cognitive dynamic system (CDS) can be a good solution for better decision making and situation understanding in non-Gaussian and nonlinear environments (NGNLE). The NGNLE situation understanding means deciding between certain known situations in...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/3/1150 |
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| author | Mahdi Naghshvarianjahromi Shiva Kumar M. Jamal Deen |
| author_facet | Mahdi Naghshvarianjahromi Shiva Kumar M. Jamal Deen |
| author_sort | Mahdi Naghshvarianjahromi |
| collection | DOAJ |
| description | The cyber processing layer of smart systems based on a cognitive dynamic system (CDS) can be a good solution for better decision making and situation understanding in non-Gaussian and nonlinear environments (NGNLE). The NGNLE situation understanding means deciding between certain known situations in NGNLE to understand the current state condition. Here, we report on a cognitive decision-making (CDM) system inspired by the human brain decision-making. The simple low-complexity algorithmic design of the proposed CDM system can make it suitable for real-time applications. A case study of the implementation of the CDS on a long-haul fiber-optic orthogonal frequency division multiplexing (OFDM) link was performed. An improvement in Q-factor of ~7 dB and an enhancement in data rate efficiency ~43% were achieved using the proposed algorithms. Furthermore, an extra 20% data rate enhancement was obtained by guaranteeing to keep the CDM error automatically under the system threshold. The proposed system can be extended as a general software-based platform for brain-inspired decision making in smart systems in the presence of nonlinearity and non-Gaussian characteristics. Therefore, it can easily upgrade the conventional systems to a smart one for autonomic CDM applications. |
| first_indexed | 2024-12-12T21:13:24Z |
| format | Article |
| id | doaj.art-f4ba2b161b274e35b1005abeafb5cb61 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-12T21:13:24Z |
| publishDate | 2020-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f4ba2b161b274e35b1005abeafb5cb612022-12-22T00:11:49ZengMDPI AGApplied Sciences2076-34172020-02-01103115010.3390/app10031150app10031150Natural Brain-Inspired Intelligence for Non-Gaussian and Nonlinear Environments with Finite MemoryMahdi Naghshvarianjahromi0Shiva Kumar1M. Jamal Deen2Dept of Electrical and Computer Engineering, McMaster University, Hamilton, ON L8S 4L8, CanadaDept of Electrical and Computer Engineering, McMaster University, Hamilton, ON L8S 4L8, CanadaDept of Electrical and Computer Engineering, McMaster University, Hamilton, ON L8S 4L8, CanadaThe cyber processing layer of smart systems based on a cognitive dynamic system (CDS) can be a good solution for better decision making and situation understanding in non-Gaussian and nonlinear environments (NGNLE). The NGNLE situation understanding means deciding between certain known situations in NGNLE to understand the current state condition. Here, we report on a cognitive decision-making (CDM) system inspired by the human brain decision-making. The simple low-complexity algorithmic design of the proposed CDM system can make it suitable for real-time applications. A case study of the implementation of the CDS on a long-haul fiber-optic orthogonal frequency division multiplexing (OFDM) link was performed. An improvement in Q-factor of ~7 dB and an enhancement in data rate efficiency ~43% were achieved using the proposed algorithms. Furthermore, an extra 20% data rate enhancement was obtained by guaranteeing to keep the CDM error automatically under the system threshold. The proposed system can be extended as a general software-based platform for brain-inspired decision making in smart systems in the presence of nonlinearity and non-Gaussian characteristics. Therefore, it can easily upgrade the conventional systems to a smart one for autonomic CDM applications.https://www.mdpi.com/2076-3417/10/3/1150autonomic decision-making systemautonomic computing layercognitive dynamic systemcognitive decision makingnon-gaussian and non-linear environmentfocus level conceptsituation understandingsmart systems |
| spellingShingle | Mahdi Naghshvarianjahromi Shiva Kumar M. Jamal Deen Natural Brain-Inspired Intelligence for Non-Gaussian and Nonlinear Environments with Finite Memory Applied Sciences autonomic decision-making system autonomic computing layer cognitive dynamic system cognitive decision making non-gaussian and non-linear environment focus level concept situation understanding smart systems |
| title | Natural Brain-Inspired Intelligence for Non-Gaussian and Nonlinear Environments with Finite Memory |
| title_full | Natural Brain-Inspired Intelligence for Non-Gaussian and Nonlinear Environments with Finite Memory |
| title_fullStr | Natural Brain-Inspired Intelligence for Non-Gaussian and Nonlinear Environments with Finite Memory |
| title_full_unstemmed | Natural Brain-Inspired Intelligence for Non-Gaussian and Nonlinear Environments with Finite Memory |
| title_short | Natural Brain-Inspired Intelligence for Non-Gaussian and Nonlinear Environments with Finite Memory |
| title_sort | natural brain inspired intelligence for non gaussian and nonlinear environments with finite memory |
| topic | autonomic decision-making system autonomic computing layer cognitive dynamic system cognitive decision making non-gaussian and non-linear environment focus level concept situation understanding smart systems |
| url | https://www.mdpi.com/2076-3417/10/3/1150 |
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