Natural Brain-Inspired Intelligence for Non-Gaussian and Nonlinear Environments with Finite Memory

Autor: M. Jamal Deen, Mahdi Naghshvarianjahromi, Shiva Kumar
Jazyk: angličtina
Rok vydání: 2020
Předmět:
autonomic computing layer
Computer science
Orthogonal frequency-division multiplexing
Gaussian
focus level concept
situation understanding
02 engineering and technology
cognitive dynamic system
lcsh:Technology
lcsh:Chemistry
symbols.namesake
Software
cognitive decision making
0203 mechanical engineering
non-gaussian and non-linear environment
0202 electrical engineering
electronic engineering
information engineering
smart systems
General Materials Science
autonomic decision-making system
Instrumentation
lcsh:QH301-705.5
Fluid Flow and Transfer Processes
Smart system
business.industry
lcsh:T
Process Chemistry and Technology
General Engineering
020302 automobile design & engineering
020206 networking & telecommunications
lcsh:QC1-999
Computer Science Applications
Nonlinear system
Upgrade
Computer engineering
lcsh:Biology (General)
lcsh:QD1-999
lcsh:TA1-2040
symbols
Algorithm design
State (computer science)
business
lcsh:Engineering (General). Civil engineering (General)
lcsh:Physics
Zdroj: Applied Sciences, Vol 10, Iss 3, p 1150 (2020)
Applied Sciences
Volume 10
Issue 3
ISSN: 2076-3417
Popis: 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.
Databáze: OpenAIRE
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