Brain-Inspired Cognitive Decision Making for Nonlinear and Non-Gaussian Environments

Abstract

The autonomic-computing layer of the smart systems based on a cognitive dynamic system (CDS) is proposed as a solution for better decision making and situation understanding in non-Gaussian and nonlinear environments (NGNLE). Here, we report on a cognitive decision-making (CDM) system inspired by the human brain decision-making process. Furthermore, it is designed based on CDS for CDM and internal commands. The simple low complexity algorithmic design of the proposed system can make it suitable for real-time applications. A case study of the implementation of the CDS was done on a long-haul fiber-optic orthogonal frequency division multiplexing (OFDM) link. An improvement in Q-factor of 3.5 dB as well as 23.3% data rate efficiency enhancement are achieved using the proposed algorithms with an extra 20% data rate enhancement by guaranteeing to keep 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.

Keywords

• Autonomic decision-making system
• autonomic computing layer
• cognitive dynamic system
• cognitive decision making
• non-Gaussian and non-linear environment
• situation understanding