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How can I learn artificial neural network? 

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Artificial neural network
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Papers (10)Insight
Journal Article•DOI
Relationships between permeability, porosity and pore throat size in carbonate rocks using regression analysis and neural networks
M R Rezaee, A Jafari, E Kazemzadeh 
01 Dec 2006-Journal of Geophysics and Engineering
136 Citations
An artificial neural network, a biologically inspired computing method which has an ability to learn, self-adjust, and be trained, provides a powerful tool in solving complex problems.
Open access
Estimating the Initial Pressure, Permeability and Skin Factor of Oil Reservoirs Using Artificial Neural Networks
Jeirani, Mohebbi 
01 Jan 2005
44 Citations
Artificial neural network, a biologically inspired computing method which has an ability to learn, self-adjust, and be trained, provides a powerful tool in solving pattern recognition problems.
Book Chapter•DOI
Acoustic Modeling of Speech Signal using Artificial Neural Network: A Review of Techniques and Current Trends
Mousmita Sarma, Kandarpa Kumar Sarma 
01 Jan 2015
11 Citations
Artificial Neural Network (ANN)s are trained by back propagating the error derivatives and therefore have the potential to learn much better models of nonlinear data.
Journal Article•DOI
Use of fuzzy neural-net model for rule generation of activated sludge process
Y.G. Du, Rajeshwar Dayal Tyagi, R. Bhamidimarri 
01 Oct 1999-Process Biochemistry
27 Citations
The artificial neural network technique is powerful because it can learn to represent complicated data patterns or data relationships between input and output variables of the system being studied.
Open access•Journal Article•DOI
Artificial neural networks and artificial organisms can predict Alzheimer pathology in individual patients only on the basis of cognitive and functional status.
Massimo Buscema, Enzo Grossi, David A. Snowdon, Piero Antuono, M. Intraligi, Guido Maurelli, Rita Savarè 
01 Jan 2004-Neuroinformatics
24 Citations
When exposed to complex data sets, artificial neural networks (ANNs) can recognize patterns, learn the relationship of different variables, and address classification tasks.
Open access•Journal Article•DOI
Tuning the Parameters of a Convolutional Artificial Neural Network by Using Covering Arrays
Humberto Pérez-Espinosa, Himer Avila-George, Josefina Rodríguez-Jacobo, Hector A. Cruz-Mendoza, Juan Martínez-Miranda, Ismael Edrein Espinosa-Curiel 
31 Dec 2016-Research on computing science
12 Citations
Artificial Neural Networks have proven to be a very powerful machine learning algorithm which can be adequate to learn successfully a variety of tasks.
Journal Article•DOI
Symbolic-neural systems and the use of hints for developing complex systems
Steven C. Suddarth, Alistair D. C. Holden 
01 Sep 1991-International Journal of Human-computer Studies \/ International Journal of Man-machine Studies
71 Citations
Neural network systems can be made to learn faster and generalize better through the addition of knowledge.
Open access•Journal Article•DOI
Fuzzy logic systems and medical applications
Elena Vlamou, Basil K. Papadopoulos 
11 Oct 2019
31 Citations
By adapting the interconnections between layers, Artificial Neural networks are able to learn.
Journal Article•DOI
Adaptive basis function for artificial neural networks
Ninan Sajeeth Philip, K. Babu Joseph 
01 Aug 2002-Neurocomputing
9 Citations
It is shown that modifying the sigmoidal basis function of a multi-layer feedforward artificial neural network using a control parameter improves the network's ability to learn.
Journal Article•DOI
An educational tool for artificial neural networks
Omer Deperlioglu, Utku Kose 
01 May 2011-Computers & Electrical Engineering
32 Citations
By using these tools, users can also understand and learn working mechanism of a typical artificial neural network, using features of different models and related learning algorithms.

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