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
This proposes a new paradigm for decision processing with potential applications in finance, education, and medicine, though it appears incremental as it builds on existing neural network and fuzzy logic concepts.
The paper tackles modeling human decision-making by introducing a functional neural network with racing neurons and fuzzy logic, aiming to transform decision computation and enable neuromorphic chips for human-machine interaction.
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.