Enhanced Prediction Accuracy in Complex Systems: An Approach Integrating Fuzzy K-Clustering and Fuzzy Neural Network
Journal Title: International Journal of Knowledge and Innovation Studies - Year 2023, Vol 1, Issue 1
Abstract
The quest for heightened precision in fuzzy system predictions has culminated in the development of an innovative model that integrates a Fuzzy K-Clustering (FKC) algorithm with a fuzzy neural network (FNN). In this approach, the novel FKC algorithm, herein introduced, undertakes the clustering of sample data. Subsequently, the clustering outcomes inform the configuration of the FNN, specifically guiding the determination of node quantities across its layers and the initial network parameters. A distinctive hybrid learning algorithm, designated as the Conjugate Recursive Least Squares (CRLS), facilitates the optimization of network parameters via distinct methods tailored to parameter types. This model underwent empirical validation using 2-minute interval average wind speed data from surface meteorological stations in China. Analytical comparisons between model predictions and actual wind speed data revealed an average absolute error of 0.2764m/s, an average absolute percentage error of 2.33%, and a maximum error of 0.6035m/s. The findings substantiate the model's superior predictive capability. This study thus presents a significant advancement in fuzzy system prediction methodologies, underscoring the potential of the FKC and FNN in complex data analysis.
Authors and Affiliations
Tichun Wang, Xianwei Wang, Hao Li
Keywords
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- EP ID EP732600
- DOI https://doi.org/10.56578/ijkis010103
- Views 61
- Downloads 0
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