首页|Research Data from De La Salle University Update Understanding of Computational Intelligence (Enhancing Fault Detection and Classification in Grid-Tied Solar Energy Systems Using Radial Basis Function and Fuzzy Logic-Controlled Data Switch)
Research Data from De La Salle University Update Understanding of Computational Intelligence (Enhancing Fault Detection and Classification in Grid-Tied Solar Energy Systems Using Radial Basis Function and Fuzzy Logic-Controlled Data Switch)
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Fuji Technology Press Ltd
New research on computational intelligence is the subject of a new report. According to news originating from Manila, Philippines, by NewsRx correspondents, research stated, “This study integrates fuzzy logic-controlled data switching and the radial basis function neural network (RBFNN) for fault detection and classification in grid-tied solar energy systems.” Financial supporters for this research include De La Salle Manila; Bulacan State University. Our news reporters obtained a quote from the research from De La Salle University: “The fuzzy logic controller filters out invalid sensor data through a data switch, ensuring that the fault detection and classification system receives reliable input. Training data were prepared through data normalization using the z-score function and principal component analysis, thereby reducing data complexity and standardizing the inputs. The resulting RBFNN model exhibited a low mean squared error with a value of 7.67×10~(-4) ,indicating its ability to classify faults based on the actual system scenarios.”
De La Salle UniversityManilaPhilippinesAsiaComputational IntelligenceFuzzy LogicMachine Learning
NETL
NSTL
Fuji Technology Press Ltd
