Yazar "Lu, Wei" seçeneğine göre listele

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    A distributed algorithm with network-independent step-size and event-triggered mechanism for economic dispatch problem
    (John Wiley and Sons Ltd, 2024) Chen, Baitong; Yang, Jianhua; Lu, Wei; Pedrycz, Witold; Sun, Changhai
    The economic dispatch problem (EDP) poses a significant challenge in energy management for modern power systems, particularly as these systems undergo expansion. This growth escalates the demand for communication resources and increases the risk of communication failures. To address this challenge, we propose a distributed algorithm with network-independent step sizes and an event-triggered mechanism, which reduces communication requirements and enhances adaptability. Unlike traditional methods, our algorithm uses network-independent step sizes derived from each agent's local cost functions, thus eliminating the need for detailed network topology knowledge. The theoretical derivation identifies a range of step size values that depend solely on the objective function's strong convexity and the gradient's Lipschitz continuity. Furthermore, the proposed algorithm is shown to achieve a linear convergence rate, assuming the event triggering threshold criteria are met for linear convergence. Numerical experiments further validate the effectiveness and advantages of our proposed distributed algorithm by demonstrating its ability to maintain good convergence characteristics while reducing communication frequency. © 2024 John Wiley & Sons Ltd.
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    A Linguistically Interpretable Deep Fuzzy Classification System With Feature Transformation and Reconstruction
    (Institute of Electrical and Electronics Engineers Inc., 2024) Zang, Zhen Sheng; Yin, Rui; Lu, Wei; Pedrycz, Witold; Zhang, Li-Yong
    Classification tasks involving tabular data often require a balance between exceptional performance and heightened interpretability. To address this challenge, we propose a linguistically interpretable deep fuzzy classification system called FFT-FFR-RBFC. The system employs a fuzzy feature transformation (FFT) unit, formed by employing a stacked architecture of multiple Takagi-Sugeno-Kang fuzzy models with nonlinear conclusions, to distill high-level fuzzy features from the input data, a rule-based fuzzy classifier unit to perform classification using these features, while a fuzzy feature reconstruction unit in tandem with the FFT to enhance the system's linguistic interpretability by remapping the high-level features back to their original space. The proposed approach is optimized by minimizing a composite loss function that balances classification and reconstruction losses, ensuring a harmonious interplay between performance and interpretability. Comprehensive evaluation across 20 diverse datasets demonstrates that the system's is exceptionally promising, particularly for high-dimensional or large-scale tabular data classification tasks, achieving superior classification performance while maintaining a high degree of interpretability. © 1993-2012 IEEE.