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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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Journal ArticleDOI
Cutting force modeling using artificial neural networks
TL;DR: In this paper, an approach for modeling cutting forces with the help of artificial neural networks is proposed using feed-forward multi-layer neural networks, trained by the error back-propagation algorithm.
Journal ArticleDOI
Modelling of residential energy consumption at the national level
TL;DR: In this article, the authors compared three methods: the engineering method (EM), the conditional demand analysis (CDA) method, and the neural network (NN) method for modeling residential energy consumption.
Proceedings ArticleDOI
Neural Wireless Sensor Networks
Frank Oldewurtel,Petri Mahonen +1 more
TL;DR: It is argued that there is a high potential with these paradigms which promise a strong impact on the future research, especially if applied as a hybrid technology.
Journal ArticleDOI
Development and validation of artificial neural network models of the energy demand in the industrial sector of the United States
Arash Kialashaki,John R. Reisel +1 more
TL;DR: In this paper, two types of numerical energy models were developed to predict the United States' future industrial energy demand. And they used an ANN (artificial neural network) technique and a MLR (multiple linear regression) technique.
Journal ArticleDOI
Performance prediction for non-adiabatic capillary tube suction line heat exchanger: an artificial neural network approach
TL;DR: In this paper, an application of the artificial neural network (ANN) model using the back propagation (BP) learning algorithm to predict the performance (suction line outlet temperature and mass flow rate) of a non-adiabatic capillary tube suction line heat exchanger, basically used as a throttling device in small household refrigeration systems.