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Fundamentals of neural networks
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The article was published on 1993-01-01 and is currently open access. It has received 1921 citations till now. The article focuses on the topics: Time delay neural network & Physical neural network.read more
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Power system voltage stability monitoring using artificial neural networks with a reduced set of inputs
Alireza Bahmanyar,A. Karami +1 more
TL;DR: In this article, an artificial neural network (ANN)-based approach for online monitoring of a voltage stability margin (VSM) in electric power systems is presented. But the main drawback of the previously published works is that they need to train a new neural network when a change in the power system topology (configuration) occurs.
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Analytical and Neural Network Models for Gas Turbine Design and Off-Design Simulation #
Andrea Lazzaretto,Andrea Toffolo +1 more
TL;DR: A gas turbine design and off-design model in which the difficulties due to lack of knowledge about stage-by-stage performance are overcome by constructing artificial machine maps through appropriate scaling techniques applied to generalized maps taken from the literature and validating them with test measurement data from real plants.
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PSO-based neural network optimization and its utilization in a boring machine
TL;DR: A particle swarm optimization technique in training a multi-layer feed-forward neural network (MFNN) which is used for a prediction model of diameter error in a boring machining achieves better machining precision with a fewer number of iterations.
Journal ArticleDOI
Modeling of manufacturing processes with ANNs for intelligent manufacturing
TL;DR: In this article, the applicability and relative effectiveness of Artificial Neural Network based models have been investigated for rapid estimation of these, invoking the function approximation capabilities of the ANN models, and the results obtained are found to correlate well with the finite element simulation data in cases of metal forming, and experimental data in case of metal cutting.
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Toward Smart Embedded Systems: A Self-aware System-on-Chip (SoC) Perspective
TL;DR: A System-on-Chip perspective is used to show how the CyberPhysical System- on-Chip (CPSoC) exemplar platform achieves self-awareness through a combination of cross-layer sensing, actuation, self-aware adaptations, and online learning.