Fuzzy Rule Learning with Linguistic Modifiers

Bahani, Khalid; Moujabbir, Mohammed; Ramdani, Mohammed

doi:10.1145/3289402.3289533

Cited by 5 publications

(2 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The rule module projects extracted clusters in all dimensions to create linguistic fuzzy rules, which gives a collection of fuzzy rules. Following that, the module uses Hamming distance to linguistically approximate the fuzzy rule with Euclidean distance and increase the accuracy using language hedges (very, plus, minus, more or less, slightly, and a little) [27]. The linguistic approximation of the fuzzy rules is illustrated in (5): where P denotes the linguistic hedge parameter and AFuni j is the MF of cluster xi * in j th dimension.…”

Section: Fuzzy Rule Learning Throught Clustringmentioning

confidence: 99%

Wind power forecasting model based on linguistic fuzzy rules

Moujabbir

Bahani²,

Ramdani³

et al. 2023

Bulletin EEI

View full text Add to dashboard Cite

The design and operationalization of a wind energy system is mainly based on wind speed and wind direction, theses parameters depend on several geographic, temporal, and climatic factors. Fluctuating factors such as climate cause irregularities in wind energy production. Therefore, wind power forecasting is necessary before using wind power systems. Furthermore, in order to make informed decisions, it is necessary to explain the system's predictions to stakeholders. The explainable artificial intelligence (XAI) provides an interactive interface for intelligent systems to interact with machines, validate their results, and trust their behavior. In this paper, we provide an interpretable system for predicting wind energy using weather data. This system is based on a two-step method for fuzzy rules learning clustering (FRLC). The first step uses subtractive clustering and a linguistic approximation to extract linguistic rules. The second step uses linguistic hedges to refine linguistic rules. FRLC is compared to with artificial neural network (ANN), random forest (RF), k-nearest neighbors (K-NN), and support vector regression (SVR) models. The experimental results show that the accuracy of FRLC is acceptable regarding the comparison models and outperform them in terms of the interpretability. In parallel with prediction, FRLC model provides a set of linguistic fuzzy rules that explain the obtained results to the stakeholders.

show abstract

Section: Fuzzy Rule Learning Throught Clustringmentioning

confidence: 99%

Wind power forecasting model based on linguistic fuzzy rules

Moujabbir

Bahani²,

Ramdani³

et al. 2023

Bulletin EEI

View full text Add to dashboard Cite

show abstract

“…The study by [17] also claimed that fuzzy rules could provide the appropriate functional relationship between input and output variables. The study operated six types of real data to build rule settings.…”

Section: Introductionmentioning

confidence: 99%

Rules Determination Based on Time-Series Data to Classify Unsupervised Cases Based on Fuzzy Expert System

2023

IJIES

View full text Add to dashboard Cite

Classification of uncertain conditions requires computational modeling to obtain exact non-vague results for making the right decision, such as opening and closing school cases during a pandemic. We cannot rely solely on normative and textual government regulations because of numerous constraints and uncertainty in implementation. Unsupervised classification techniques can deal with such issues without needing prior references that contain definitive hesitancy. This motivates us to use a fuzzy system based on knowledge-based composition rules for complex problems such as the dynamics of COVID-19 because of its ability to adapt to changes and uncertainties. Therefore, we construct rules based on knowledge about COVID-19 to the issue of opening/closing schools using three fuzzy approaches: conventional fuzzy, intuitionistic fuzzy system (IFS), and fuzzy c-means (FCM). We can demonstrate a correlation between the number of school openings and the COVID-19 dynamics by utilizing the fuzzy approach to reduce the degree of hesitance. Experiments on available public time-series datasets demonstrate that the IFS is more efficient in forming rigidly distinct two classes. The results indicate that the accuracy of IFS is 99.47%, FCM is 91.28, and conventional FS is 84.33%, including the IFS silhouette score, which is higher than the others, at 0.91 or closer to 1, indicating excellent classification results. IFS is less superior in running time, while FCM is the fastest. This is because there are multiple stages in the IFS by considering non-membership functions.

show abstract