A perceptual fuzzy neural model

Abstract

We introduce a fuzzy neural model which is more intuitive and general than the traditional weighted sum/squashing function neuron model. Positively and negatively causal inputs are separately aggregated using operators that are selected to suit the particular application. The aggregations are then combined using a simple arithmetic transformation. We outline the computational process when inputs and importance weights are vocabulary words modelled as interval type-2 fuzzy sets, and illustrate on predictions of gold price changes.