Metasurface for artificial neural computing (Conference Presentation)

2019 ◽  
Author(s):  
Zongfu Yu
Keyword(s):  
Neural Computing ◽  
Artificial Neural

Nonlinear Nanophotonic Media for Artificial Neural Computing

2019 ◽  
Author(s):  
Erfan Khoram ◽  
Ang Chen ◽  
Dianjing Liu ◽  
Qiqi Wang ◽  
Ming Yuan ◽  
...  
Keyword(s):  
Neural Computing ◽  
Artificial Neural

scholarly journals MODELLING MASONRY CREW PRODUCTIVITY USING TWO ARTIFICIAL NEURAL NETWORK TECHNIQUES

2014 ◽  
Vol 21 (2) ◽  
pp. 231-238 ◽  
Author(s):  
Ibrahim Halil Gerek ◽  
Ercan Erdis ◽  
Gulgun Mistikoglu ◽  
Mumtaz Usmen
Keyword(s):  
Neural Network ◽  
Labour Productivity ◽  
Model Performance ◽  
Percentage Error ◽  
Neural Computing ◽  
Feed Forward Neural Network ◽  
Radial Basis Neural Network ◽  
Artificial Neural ◽  
Performance Research ◽  
Better Than

Artificial neural networks have been effectively used in various civil engineering fields, including construction management and labour productivity. In this study, the performance of the feed forward neural network (FFNN) was compared with radial basis neural network (RBNN) in modelling the productivity of masonry crews. A variety of input factors were incorporated and analysed. Mean absolute percentage error (MAPE) and correlation coefficient (R) were used to evaluate model performance. Research results indicated that the neural computing techniques could be successfully employed in modelling crew productivity. It was also found that successful models could be developed with different combinations of input factors, and several of the models which excluded one or more input factors turned out to be better than the baseline models. Based on the MAPE values obtained for the models, the RBNN technique was found to be better than the FFNN technique, although both slightly overestimated the masons’ productivity.

A Hybrid Approach for Neural Network in Pattern Storage

2021 ◽  
pp. 43-49
Author(s):  
Kumud Sachdeva ◽  
◽  
Shruti Aggarwal ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Computational Complexity ◽  
Hybrid Approach ◽  
Neural Computing ◽  
Generalization Ability ◽  
Suboptimal Solutions ◽  
Artificial Neural ◽  
Made In

Your mind does not manufacture mind. Your mind forms neural networks. Neural networks had been effectively carried out to numerous sample garage and type troubles in phrases in their mastering ability, excessive discrimination electricity, and exceptional generalization ability. The achievement of many mastering schemes isn't always assured, however, seeing that algorithms like backpropagation have many drawbacks like stepping into the nearby minima, for that reason imparting suboptimal solutions. In the case of classifying big sets and complicated patterns, the traditional neural networks are afflicted by numerous problems inclusive of the dedication of the shape and length of the network, the computational complexity, and so on. This paper introduces the neural computing techniques especially radial foundation features network. Various upgrades and trends made in an artificial neural network for rushing up training, keeping off neighborhood minima, growing the generalization capacity, and different capabilities are reviewed.

scholarly journals Nonlinear Structure based Artificial Neural Computing for Upstream Flow Functional Models

2019 ◽  
Vol 15 (15) ◽  
Author(s):  
Tanveer Ahmed Siddiqi ◽  
Syed Inayatullah ◽  
Syed Ahmad Hassan ◽  
Saba Naz ◽  
Syed Tanweer Iqbal ◽  
...  
Keyword(s):  
Neural Computing ◽  
Nonlinear Structure ◽  
Functional Models ◽  
Artificial Neural ◽  
Upstream Flow

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural
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