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Radial systems employ time-delay overcurrent relays to reduce load interruptions. When a fault occurs, the nearest breaker opens first, while upstream breakers remain closed due to longer delay settings. This approach ensures minimal disruption to the rest of the system.
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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
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Linear Approximation in Frequency Domain01:26

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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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Multi-input and Multi-variable systems01:22

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Radius of Gyration of an Area01:12

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Related Experiment Video

Updated: Jan 2, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

709

Adaptive Learning for Robust Radial Basis Function Networks.

Abd-Krim Seghouane, Navid Shokouhi

    IEEE Transactions on Cybernetics
    |December 4, 2019
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a robust method for training Radial Basis Function Networks (RBFNs) by adapting a generalized Kullback-Leibler divergence. The new approach improves mean-squared error performance, especially with noisy data.

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    Deep Neural Networks for Image-Based Dietary Assessment
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    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Signal Processing

    Background:

    • Least-squares estimation is common for Radial Basis Function Network (RBFN) output layer parameter estimation.
    • This method is equivalent to maximum-likelihood estimation under Gaussian noise assumptions.
    • Outliers in data can significantly degrade the performance of standard estimation techniques.

    Purpose of the Study:

    • To develop a robust estimation method for RBFN output layer parameters.
    • To improve network performance in the presence of non-Gaussian noise and outliers.
    • To enhance the adaptability of RBFNs to diverse noise distributions.

    Main Methods:

    • Proposed a novel approach using a generalized Kullback-Leibler (KL) divergence.
    • Developed a surrogate-likelihood function robust to various noise distributions.
    • Implemented an adaptive learning algorithm for RBFN training.

    Main Results:

    • The proposed method demonstrated superior performance compared to standard approaches in signal processing experiments.
    • Consistent improvements in mean-squared error (MSE) were observed across artificially generated and real-world datasets.
    • The algorithm showed enhanced robustness against different noise conditions and outliers.

    Conclusions:

    • The generalized KL divergence-based method offers a more robust alternative for RBFN parameter estimation.
    • The adaptive learning algorithm effectively handles broader classes of noise, outperforming existing robust training methods.
    • This work contributes to more reliable machine learning models in real-world applications with imperfect data.