An Approach to Generating Fuzzy Rules for a Fuzzy Controller Based on the Decision Tree Interpretation

Axioms, Год журнала: 2025, Номер 14(3), С. 196 - 196

Опубликована: Март 6, 2025

This article describes solutions to control problems using fuzzy logic, which facilitates the development of decision support systems across various fields. However, addressing this task through manual creation rules in specific fields necessitates significant expert knowledge. Machine learning methods can identify hidden patterns. A key novelty approach is algorithm for generating a controller, derived from interpreting tree. The proposed allows quality actions organizational and technical be enhanced. presents an example set analysis tree model. constructing rule-based (FRBSs). Additionally, it autogenerates membership functions linguistic term labels all input output parameters. machine model FRBS obtained were assessed coefficient determination (R2). experimental results demonstrated that constructed performed on average 2% worse than original While could enhanced by optimizing functions, topic falls outside scope current article.

Язык: Английский

Exploiting adaptive neuro-fuzzy inference systems for cognitive patterns in multimodal brain signal analysis

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 16, 2025

The analysis of cognitive patterns through brain signals offers critical insights into human cognition, including perception, attention, memory, and decision-making. However, accurately classifying these remains a challenge due to their inherent complexity non-linearity. This study introduces novel method, PCA-ANFIS, which integrates Principal Component Analysis (PCA) Adaptive Neuro-Fuzzy Inference Systems (ANFIS), enhance pattern recognition in multimodal signal analysis. PCA reduces the dimensionality EEG data while retaining salient features, enabling computational efficiency. ANFIS combines adaptability neural networks with interpretability fuzzy logic, making it well-suited model non-linear relationships within signals. Performance metrics our proposed such as accuracy, sensitivity, These additions highlight effectiveness method provide concise summary findings. achieves superior classification performance, an unprecedented accuracy 99.5%, significantly outperforming existing approaches. Comprehensive experiments were conducted using diverse dataset, demonstrating method's robustness sensitivity. integration addresses key challenges analysis, artifact contamination non-stationarity, ensuring reliable feature extraction classification. research has significant implications for both neuroscience clinical practice. By advancing understanding processes, PCA-ANFIS facilitates accurate diagnosis treatment disorders neurological conditions. Future work will focus on testing approach larger more datasets exploring its applicability domains neurofeedback, neuromarketing, brain-computer interfaces. establishes capable tool precise efficient processing.

Язык: Английский

Two methodologies for brain signal analysis derived from Freeman Neurodynamics

Frontiers in Systems Neuroscience, Год журнала: 2025, Номер 19

Опубликована: Апрель 15, 2025

Here, Freeman Neurodynamics is explored to introduce the reader challenges of analyzing electrocorticogram or electroencephalogram signals make sense two things: (a) how brain participates in creation knowledge and meaning (b) differentiate between cognitive states modalities dynamics. The first addressed via a Hilbert transform-based methodology second Fourier transform methodology. These methodologies, it seems us, conform with systems' neuroscience views, models, signal analysis methods that Walter J. III used left for us as his legacy.

Язык: Английский