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Mostrando ítems 11-16 de 16
Artículo
Fault Diagnosis of Metro Traction Power Systems Using A Modified Fuzzy Reasoning Spiking Neural P System
(Romanian Academy, Section for Information Science and Technology, 2015)
This paper presents the application of a modified fuzzy reasoning spiking neural P systems (MFRSN P system, for short) to fault diagnosis of metro traction power supply systems. In MFRSN P systems, three types of neurons ...
Artículo
Fault Section Estimation of Power Systems with Optimization Spiking Neural P Systems
(Romanian Academy, Section for Information Science and Technology, 2015)
An optimization spiking neural P system (OSNPS) provides a novel way to directly use a P system to solve optimization problems. This paper discusses the practical application of OSNPS for the first time and uses it to ...
Ponencia
Simulating FRSN P Systems with Real Numbers in P-Lingua on sequential and CUDA platforms
(Springer, 2015)
Fuzzy Reasoning Spiking Neural P systems (FRSN P systems, for short) is a variant of Spiking Neural P systems incorporating fuzzy logic elements that make it suitable to model fuzzy diagnosis knowledge and reasoning ...
Artículo
Fuzzy reasoning spiking neural P systems revisited: A formalization
(Elsevier, 2017)
Research interest within membrane computing is becoming increasingly interdisciplinary.In particular, one of the latest applications is fault diagnosis. The underlying mechanismwas conceived by bridging spiking neural P ...
Artículo
Fuzzy reasoning spiking neural P system for fault diagnosis
(Elsevier, 2013)
Spiking neural P systems (SN P systems) have been well established as a novel class of distributed parallel computing models. Some features that SN P systems possess are attractive to fault diagnosis. However, handling ...
Artículo
One-shot fault diagnosis of 3D printers through improved feature space learning
(IEEE Computer Society, 2020)
Signal acquisition from mechanical systems working in faulty conditions is normally expensive. As a consequence, supervised learning-based approaches are hardly applicable. To address this problem, a one-shot learning-based ...