Results 11 to 20 of about 2,089,547 (271)
Artificial Neural Networks [PDF]
, 2015 Polynomial classifiers can model decision surfaces of any shape; and yet their practical utility is limited because of the easiness with which they overfit noisy training data, and because of the sometimes impractically high number of trainable parameters.
Gulko, Illya +2 more
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A geographically weighted artificial neural network
International Journal of Geographical Information Science, 2021 While recent developments have extended geographically weighted regression (GWR) in many directions, it is usually assumed that the relationships between the dependent and the independent variables are linear.
Julian Hagenauer, M. Helbich
semanticscholar +1 more source
METODE KOMPARASI ARTIFICIAL NEURAL NETWORK PADA PREDIKSI CURAH HUJAN - LITERATURE REVIEW
Jurnal Tekno Insentif, 2020 Abstrak - Penelitian untuk mencari model prediksi curah hujan yang akurat di berbagai bidang sudah banyak dilakukan, maka dilakukan di-review kembali guna membantu proses penyaliran dalam perusahaan tambang.
Herlina Jayadianti +3 more
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Advances in Civil Engineering, 2022 Wind flow on a bluff body is a complex and nonlinear phenomenon that has been mainly studied experimentally or analytically. Several mathematical methods have been developed to predict the wind-induced pressure distribution on bluff bodies; however, most
Josué U. Rodríguez-Alcántara +2 more
doaj +1 more source
Holography in artificial neural networks [PDF]
Nature, 1990 The dense interconnections that characterize neural networks are most readily implemented using optical signal processing. Optoelectronic 'neurons' fabricated from semiconducting materials can be connected by holographic images recorded in photorefractive crystals. Processes such as learning can be demonstrated using holographic optical neural networks.
Psaltis, Demetri +3 more
openaire +5 more sources
Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science [PDF]
Nature Communications, 2017 Through the success of deep learning in various domains, artificial neural networks are currently among the most used artificial intelligence methods. Taking inspiration from the network properties of biological neural networks (e.g.
D. Mocanu +5 more
semanticscholar +1 more source
Artificial Neural Network Controller for Reducing the Total Harmonic Distortion (THD) in HVDC [PDF]
, 2018 A neural network based space vector modulation (SVM) of voltage source inverter is proposed. The voltage source converter (VSC) is highly used in high voltage direct current (HVDC) transmission so that a detailed analysis and transmission of this system ...
Hamoodi, A. N. (Ali) +1 more
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A Review of the Artificial Neural Network Models for Water Quality Prediction
Applied Sciences, 2020 Water quality prediction plays an important role in environmental monitoring, ecosystem sustainability, and aquaculture. Traditional prediction methods cannot capture the nonlinear and non-stationarity of water quality well.
Yingyi Chen +4 more
semanticscholar +1 more source
Prediction of Ultimate Bearing Capacity of Skirted Footing Resting on Sand Using Artificial Neural Networks [PDF]
Journal of Soft Computing in Civil Engineering, 2018 The paper presents the prediction of ultimate bearing capacity of different regular shaped skirted footing resting on sand using artificial neural network.
Rakesh Dutta +2 more
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Analysis of Artificial Neural-Network [PDF]
International Journal of Trend in Scientific Research and Development, 2018 An Artificial Neural Network ANN is a computational model that is inspired by the way biological neural networks in the human brain process information. Artificial Neural Networks have generated a lot of excitement in Machine Learning research and industry, thanks to many breakthrough results in speech recognition, computer vision and text processing ...
Rajesh CVS, Nadikoppula Pardhasaradhi
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