Noise Parameter Estimation for Non-Singleton Fuzzy Logic Systems

Abstract

Real-world environments face a wide range of noise (uncertainty) sources and gaining insight into the level of noise is a critical part of many applications. While Non-Singleton Fuzzy Logic Systems (NSFLSs), in particular recently introduced advanced variants such as centroid-based NSFLSs have the capacity to handle known quantities of uncertainty, thus far, the actual level of uncertainty has had to be defined a priori -i.e. prior to run time of a system or controller. This paper does not focus on such advances within the architecture of NSFLSs, but focuses on a novel two-stage approach for uncertainty handling in fuzzy logic systems which integrates: (i) estimation of noise levels and (ii) the appropriate handling of the noise based on this estimate, by means of a dynamically configured NSFLS. As initial evaluation of the approach, two chaotic nonlinear time series (Mackey-Glass and Lorenz), as well as a real-world Darwin sea level pressure series prediction fuzzy logic systems are implemented and compared to commonly used procedures. The results indicate that the proposed strategy of integrating uncertainty/noise estimation with the capacity of non-singleton fuzzy logic systems has the potential to deliver performance benefits in real-world applications without requiring a priori information on noise levels and thus delivers a first step towards smart, noiseadaptive non-singleton fuzzy logic systems and controllers.

Uncertainty-Aware Forecasting of Renewable Energy Sources

“…• Step 1 FLS Design: A Mamdani [19] FLS is designed to conduct time series prediction. Standard approaches to FLS time series prediction are applied, including input FS design [16]- [18], rule set generation [24], etc. We note that in future the design process may be tailored specifically to optimally generating interval-valued predictions, but we are not addressing this in this pilot paper.…”