Functional neural network for decision processing, a racing network of programmable neurons with fuzzy logic where the target operating model relies on the network itself

TL;DR

This paper introduces a novel functional neural network model that mimics human decision-making through racing neurons with fuzzy logic, aiming to enhance decision processing and inspire future neuromorphic hardware.

Contribution

The paper presents a new neural network architecture with racing neurons and fuzzy logic, offering a fresh approach to modeling decision-making processes.

Findings

Mathematical formulation of racing neurons

Implementation of fuzzy logic in decision group processes

Potential applications in finance, education, and medicine

Abstract

In this paper, we are introducing a novel model of artificial intelligence, the functional neural network for modeling of human decision-making processes. This neural network is composed of multiple artificial neurons racing in the network. Each of these neurons has a similar structure programmed independently by the users and composed of an intention wheel, a motor core and a sensory core representing the user itself and racing at a specific velocity. The mathematics of the neuron's formulation and the racing mechanism of multiple nodes in the network will be discussed, and the group decision process with fuzzy logic and the transformation of these conceptual methods into practical methods of simulation and in operations will be developed. Eventually, we will describe some possible future research directions in the fields of finance, education and medicine including the opportunity to design an intelligent learning agent with application in business operations supervision. We believe that this functional neural network has a promising potential to transform the way we can compute decision-making and lead to a new generation of neuromorphic chips for seamless human-machine interactions.