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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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Several non-linear models in estimating air-overpressure resulting from mine blasting
Mahdi Hasanipanah,Danial Jahed Armaghani,Hossein Khamesi,Hassan Bakhshandeh Amnieh,Saber Ghoraba +4 more
TL;DR: This research presents several non-linear models including empirical, artificial neural network (ANN), fuzzy system and adaptive neuro-fuzzy inference system (ANFIS) to estimate air-overpressure (AOp) resulting from mine blasting and results obtained indicate higher performance capacity of the ANFIS technique to estimate AOp compared to other implemented methods.
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Effects of learning parameters on learning procedure and performance of a BPNN
TL;DR: The effects of changing learning parameters on the learning procedure and performance of back-propagation neural networks used to pick seismic arrivals show that change mainly affects the speed of convergence of the learning procedures, and does not affect the BPNN structure and its overall performance.
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Persian sign language (PSL) recognition using wavelet transform and neural networks
TL;DR: A system for recognizing static gestures of alphabets in Persian sign language (PSL) using Wavelet transform and neural networks (NN) and this system only requires the images of the bare hand for the recognition.
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Application of two intelligent systems in predicting environmental impacts of quarry blasting
Danial Jahed Armaghani,Mohsen Hajihassani,Masoud Monjezi,Edy Tonnizam Mohamad,Aminaton Marto,Mohammad Reza R. Moghaddam +5 more
TL;DR: This paper aimed to predict the blasting environmental impacts in granite quarry sites through two intelligent systems, namely artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS).
Journal ArticleDOI
Opening the black box of neural networks for remote sensing image classification
Fang Qiu,John R. Jensen +1 more
TL;DR: The improved neuro-fuzzy system incorporates the best of both technologies and compensates for the shortcomings of each and was significantly superior to those of the back-propagation based neural network and the maximum likelihood approaches.