Results 91 to 100 of about 1,355,935 (279)
Adaptive Neural Sliding Mode Control of Active Power Filter
Journal of Applied Mathematics, 2013 A radial basis function (RBF) neural network adaptive sliding mode control system is developed for the current compensation control of three-phase active power filter (APF). The advantages of the adaptive control, neural network control, and sliding mode
Juntao Fei, Zhe Wang
doaj +1 more source
Bus Travel TIME in the Mixed Traffic Based on Statistica Neural Network [PDF]
, 2010 This paper presents the assessment of a number of factors affecting bus travel time and a relationship model between those factors and bus travel time. Statistica Neural Network (SNN) tool is used to solve this complex phenomenon.
Kamaruddin, I. (Ibrahim) +3 more
core
Ensemble‐based soil liquefaction assessment: Leveraging CPT data for enhanced predictions
Civil Engineering Design, Volume 7, Issue 1, Page 23-35, March 2025.Abstract This study focuses on predicting soil liquefaction, a critical phenomenon that can significantly impact the stability and safety of structures during seismic events. Accurate liquefaction assessment is vital for geotechnical engineering, as it informs the design and mitigation strategies needed to safeguard infrastructure and reduce the risk ...
Arsham Moayedi Far, Masoud Zare
wiley +1 more source
Radial basis function network learns ceramic processing and predicts related strength and density [PDF]
Radial basis function (RBF) neural networks were trained using the data from 273 Si3N4 modulus of rupture (MOR) bars which were tested at room temperature and 135 MOR bars which were tested at 1370 C.
Baaklini, George Y. +3 more
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Membrane Engineering for Battery Systems: Bridging Design Principles and Frontier Applications
ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.The review emphasizes membrane separators' role in battery performance and safety, covering redox flow, lithium‐ion, and solid‐state batteries. It reviews advances in membrane materials (e.g., polymer electrolytes, hybrid composites) and ion transport mechanisms, while addressing challenges like dendrite growth and crossover losses.
Xiaoqun Zhou +3 more
wiley +1 more source
RBF-MLMR: A Multi-Label Metamorphic Relation Prediction Approach Using RBF Neural Network
IEEE Access, 2017 Metamorphic testing has been successfully used in many different fields to solve the test oracle problem. However, how to find a set of appropriate metamorphic relations for metamorphic testing remains a complicated and tedious task.
Pengcheng Zhang +3 more
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AI‐driven circular economy optimization in waste management: A review of current evidence
Environmental Progress &Sustainable Energy, EarlyView.Abstract The integration of artificial intelligence (AI) and machine learning (ML) in waste management has the potential to significantly advance circular economy objectives by enhancing efficiency, reducing waste, and optimizing resource recovery. However, realising these benefits depends on addressing significant technical, economic, and systemic ...
David Bamidele Olawade +3 more
wiley +1 more source
Gong-kuang zidonghua, 2013 In view of problem that eddy-current sensor cannot reflect measured physical quantity accurately caused by higher nonlinear of output characteristic parameter, the paper proposed a scheme of using RBF neural network to fit output characteristic parameter
YOU Wen-jian, LIANG Bing, LI Yin-jun
doaj
气体物理In this paper, the radial basis function (RBF) neural network was modified by data assimilation method to improve the modeling accuracy of high-dimensional aerodynamics. A correction factor γ was introduced into the kernel function of the traditional RBF
Ying ZHANG +3 more
doaj +1 more source
AI‐based localization of the epileptogenic zone using intracranial EEG
Epilepsia Open, EarlyView.Abstract Artificial intelligence (AI) is rapidly transforming our lives. Machine learning (ML) enables computers to learn from data and make decisions without explicit instructions. Deep learning (DL), a subset of ML, uses multiple layers of neural networks to recognize complex patterns in large datasets through end‐to‐end learning.
Atsuro Daida +5 more
wiley +1 more source