Determining Firing Strengths Through a Novel Similarity Measure to Enhance Uncertainty Handling in Non-singleton Fuzzy Logic Systems

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

2019

Self Cite

Most real-world environments are subject to different sources of uncertainty which may vary in magnitude over time. We propose that while Type-1 (T1) Non-Singleton Fuzzy Logic System (NSFLSs) have the potential to tackle uncertainty within the input Fuzzy Sets (FSs), Type-2 (T2) input FSs provide the ability to also capture variation in uncertainty levels by means of their extra degrees of freedom. Specifically, in this paper, we propose a strategy to design Interval Type-2 (IT2) input Membership Functions (MFs) in an online manner to ensure the parameters of input MFs are updated dynamically, thus capturing varying levels of uncertainty affecting systems' inputs. In this strategy, first, uncertainty detection is performed over a given time-frame (the Uncertainty Estimation Time-frame) and Type-1 (T1) input MFs are constructed by utilising the detected uncertainty level. Second, the variation of the uncertainty levels over a sliding window (the Uncertainty Variation Window) is used to capture the degree of variation in the detected uncertainty levels over time, which in turn informs the size of the Footprint of Uncertainty (FOU) of the IT2 MF associated with the T1 principal MF. Using time-series prediction experiments as an initial evaluation and demonstration platform for the proposed architecture, we show that the proposed strategy of designing IT2 input MFs has the potential to deliver performance benefits. Specifically, it allows systems to not only adapt to specific uncertainty levels but also to be more resilient to the variation of said uncertainty levels over time, thus offering a pathway to robust performance in real-world applications.

Section: Related Literature In Nsflssmentioning

confidence: 99%

Leveraging IT2 Input Fuzzy Sets in Non-Singleton Fuzzy Logic Systems to Dynamically Adapt to Varying Uncertainty Levels

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

2019

Self Cite

“…Thereby, a noisy set can be obtained for each (t) in x t . In the literature, there are a number of studies which deal with uncertainty in time series data by means of NSFLSs [6], [8]- [16]. Generally, it is assumed that noise levels (σ noise ) are known in advance or it is calculated by using the known SNR and σ nf values (7).…”

Section: Noise Adding To Time Seriesmentioning

confidence: 99%

“…1. As mentioned earlier, rather using advanced architecture NSFLSs, such as centroid or similarity based systems [6], [15], [16], in the inference step of FLSs, the standard minimum t-norm and max t-conorm operators are used and centroid defuzzification is utilised in the last step of FLSs. The same number of discretisation level (500) is used for all FLSs.…”

Section: Noise Adding To Time Seriesmentioning

confidence: 99%

“…Due to the fact that inputs are commonly corrupted by noise, non-singleton fuzzy sets have potentially designed to capture the existed noise in input data, and so may provide better results than SFLSs for the same number of rules [2]- [7]. To illustrate uncertainty handling with the non-singleton designs, the extensive studies in time series prediction are carried out [6], [8]- [16]. In these studies, it is assumed that the additive noise level (σ noise ) is already either a priori known or can be calculated by using the known values of the signal to noise ratio (SNR) and standard deviations of a noise free set (σ nf ).…”

Section: Introductionmentioning

confidence: 99%

“…Secondly, the gathered noise levels are proceeded to NSFLSs to set input MFs parameters and NSFLSs time series prediction results are compared. While in the literature, there are a number of works on advanced non-singleton architecture, such as centroid or similarity based systems [6], [15], [16]. This paper does not focus on such advances within the architecture of NSFLSs, but focuses on specifically one key problem detecting noise levels to define NSFLSs parameters at run time.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Noise Parameter Estimation for Non-Singleton Fuzzy Logic Systems

2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

2018

Self Cite

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

2020 IEEE International Conference on Big Data and Smart Computing (BigComp)

et al. 2020

Smart grid systems are designed to enable the efficient capture and intelligent distribution of electricity across a distributed set of utilities. They are an essential component of increasingly important renewable energy sources, where it is vital to forecast the levels of energy being fed into and drawn from the grid. However, because of the high levels of uncertainty affecting real-world environments, accurate forecasting for example of wind power generation -being directly dependent on meteorological parameters and climatic conditions -is extremely challenging. Fuzzy Logic systems are frequently used in control systems to leverage their capacity for handling varying levels of uncertainty. In most cases, while uncertainty affecting the systems is captured in fuzzy sets (FSs), the final output of such systems is reduced to a crisp number (e.g. a control output). The latter process, while providing an efficient pathway to generating a specific control output, at the same time implies substantial information loss, as the uncertainty information captured in the FS outputs of these systems is effectively discarded. In this paper, we explore the potential of Mamdani fuzzy logic system based forecasting in order to generate not only a numeric forecast of the energy generated, but to also generate uncertainty intervals around said forecast indicating the level of uncertainty associated with the prediction. The proposed model is explored using both synthetic and smart-grid specific real-world (wind power) time series datasets. The results of the study indicate that utilising the 'complete' FS output can provide valuable additional information in terms of the reliability of the forecast without any extra computational cost. At a general level, the approach indicates strong potential for leveraging the uncertainty information in fuzzy system outputs -which is commonly discarded -in real world applications.