Results 91 to 100 of about 59,820 (274)

Disturbance‐Resilient Voltage/Power‐Quality Management for Islanded Inverters Using Self‐Tuning Type‐3 Fuzzy Controller

Energy Science &Engineering, EarlyView.
An adaptive fuzzy controller using an interval type‐3 fuzzy logic system replaces the SMC switching term to mitigate chattering while preserving global stability for islanded inverters. Simulations show lower THD, greater robustness to disturbances and parameter variations, and improved voltage‐tracking accuracy, with applicability to other uncertain ...
Man‐Wen Tian, Jun‐Bo Mao, Nurkhat Zhakiyev, Didar Yedilkhan, Ruslan Omirgaliyev, Du Changdong, Amirhossein Khosravi Sarvenoee, Ardashir Mohammadzadeh   +7 more
wiley   +1 more source

High-Dimensional Aerodynamic Modeling Prediction Based on Modified RBF Neural Network with Data Assimilation

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, Xin ZHANG, Weiguo ZHANG, Zichen DENG   +3 more
doaj   +1 more source

A New Localization Algorithm Based on Taylor Series Expansion for NLOS Environment

Cybernetics and Information Technologies, 2016
In Non-Line-Of-Sight (NLOS) environment, location accuracy of Taylorseries expansion location algorithm degrades greatly. A new Taylor-series expansion location algorithm based on self-adaptive Radial-Basis-Function (RBF) neural network is proposed in ...
Ren Jin, Chen Jingxing, Bai Wenle
doaj   +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, Xuewu Zhou, Patrizio Pelliccione, Hareton Leung   +3 more
doaj   +1 more source

A neuro-fuzzy approach as medical diagnostic interface [PDF]

, 2010
In contrast to the symbolic approach, neural networks seldom are designed to explain what they have learned. This is a major obstacle for its use in everyday life.
Brause, Rüdiger W., Friedrich, F.
core  

Real‐Time Data‐Driven Fault Diagnosis of Photovoltaic Arrays Using an Edge‐Server Machine‐Learning Framework

Energy Science &Engineering, EarlyView.
A real‐time, data‐driven framework detects and classifies photovoltaic array faults using edge sensing and server‐side machine learning. Ensemble tree models achieve near‐perfect accuracy with low latency, enabling practical, low‐cost deployment for reliable PV monitoring and intelligent maintenance.
Premkumar Manoharan, Sowmya Ravichandran, Deepak Kumar Sinha, Chandran Ramakrishnan, Omar Almomani   +4 more
wiley   +1 more source

Daily Residential Natural Gas Demand Forecasting Using Machine Learning Regression: Comparative Evaluation With a Case Study in Qazvin Province, Iran

Energy Science &Engineering, EarlyView.
This graphical abstract summarizes the proposed framework for improving short‐term residential natural gas consumption forecasting by integrating a novel socioeconomic indicator, the subscription growth ratio (SGR), with conventional meteorological variables.
Ali Pirzad, Mostafa Khanzadi
wiley   +1 more source

Forecasting the geomagnetic activity of the Dst Index using radial basis function networks [PDF]

, 2006
The Dst index is a key parameter which characterises the disturbance of the geomagnetic field in magnetic storms. Modelling of the Dst index is thus very important for the analysis of the geomagnetic field.
Balikhin, M.A., Billings, S.A., Wei, H.L., Zhu, D.Q.   +3 more
core   +1 more source

Using DSGE and Machine Learning to Forecast Public Debt for France

Journal of Forecasting, EarlyView.
ABSTRACT Forecasting public debt is essential for effective policymaking and economic stability, yet traditional approaches face challenges due to data scarcity. While machine learning (ML) has demonstrated success in financial forecasting, its application to macroeconomic forecasting remains underexplored, hindered by short historical time series and ...
Emmanouil Sofianos, Thierry Betti, Theophilos Papadimitriou, Amélie Barbier‐Gauchard, Periklis Gogas   +4 more
wiley   +1 more source

Machine Learning‐Based Estimation of Reference Evapotranspiration and Crop Coefficients for Wheat Under Diverse Climatic Conditions

Irrigation and Drainage, EarlyView.
ABSTRACT Accurate estimation of reference evapotranspiration (ET0) and crop coefficients (Kc) is critical for irrigation planning, particularly in data‐limited regions where agriculture dominates freshwater consumption. Although machine learning (ML) methods have been widely applied to ET0 and Kc estimation, most studies address these parameters ...
Ilker Angin, Hasan Er, Mete Yaganoglu, Muhammed Sungur Demir, Abdullah Muratoglu   +4 more
wiley   +1 more source