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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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Personalised viewing-time prediction in museums
Fabian Bohnert,Ingrid Zukerman +1 more
TL;DR: This article investigates predictive user models for personalised prediction of museum visitors’ viewing times at exhibits, considers two content-based models and a nearest-neighbour collaborative filter, and develops a collaborative model based on the theory of spatial processes which relies on a notion of distance between exhibits.
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Hourly predictive artificial neural network and multivariate regression trees models of Ganoderma spore concentrations in Rzeszów and Szczecin (Poland).
TL;DR: The aerobiology of Ganoderma basidiospores in two cities in Poland was examined using the volumetric method, and models indicated that atmospheric phenomenon, hour and relative humidity were the most important variables influencing spore content.
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Modified imperialist competitive algorithm-based neural network to determine shear strength of concrete beams reinforced with FRP
TL;DR: To model shear strength of concrete beams reinforced with FRP bars and without stirrups, an artificial neural network was trained by an ensemble of Levenberg-Marquardt and imperialist competitive algorithms and the results suggested superior accuracy of model compared to equations available in specifications and literature.
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Automation in Mössbauer spectroscopy data analysis
TL;DR: The present article reviews the main progress in automation of Mossbauer spectroscopy data analysis by using genetic algorithms, fuzzy logic, and artificial neural networks.
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Neural network-based robot visual positioning for intelligent assembly
TL;DR: This work presents a visual positioning system that addresses feature extraction issues for a class of objects that have smooth or curved surfaces and learns the complex relationship between the robot’s pose displacements and the observed variations in the image features.