A New Interval Type-2 Fuzzy Aggregation Approach for Combining Multiple Neural Networks in Clustering and Prediction of Time Series

Inspired by how some cognitive abilities affect the human decision-making process, the proposed approach combines neural networks with type-2 fuzzy systems. The proposal consists of combining computational models of artificial neural networks and fuzzy systems to perform clustering and prediction of time series corresponding to the population, urban population, particulate matter (PM2.5), carbon dioxide (CO2), registered cases and deaths from COVID-19 for certain countries. The objective is to associate these variables by country based on the identification of similarities in the historical information for each variable. The hybrid approach consists of computationally simulating the behavior of cognitive functions in the human brain in the decision-making process by using different types of neural models and interval type-2 fuzzy logic for combining their outputs. Simulation results show the advantages of the proposed approach, because starting from an input data set, the artificial neural networks are responsible for clustering and predicting values of multiple time series, and later a set of fuzzy inference systems perform the integration of these results, which the user can then utilize as a support tool for decision-making with uncertainty.

Automated summary

This research combines neural networks with type-2 fuzzy systems to perform clustering and prediction of time series data related to population, urban population, PM2.5, CO2, registered cases, and deaths from COVID-19 for certain countries. The approach simulates the behavior of cognitive functions in the human brain using different types of neural models and interval type-2 fuzzy logic for decision-making process. The results show the advantages of this approach in clustering, predicting, and integrating multiple time series data for decision-making with uncertainty.