The concept of using neural networks
so that computers are able to recognise patterns was introduced in the 1980s.
Using an optical chip to perform neural network
computations more efficiently than is possible with digital computers could allow more complex problems to be solved, said research team leader Shanhui Fan of Stanford University.
What we want to see here is, if we show a bunch of examples of these particles, many many different particles, to a neural network
, whether the neural network
can develop 'intuition' for it."
are good at solving pattern recognition problems.
Aihara, "Complex-valued multistate associative memory with nonlinear multilevel functions for gray-level image reconstruction," IEEE Transactions on Neural Networks
Competitive neural networks
that use an unsupervised learning algorithm were also trained and tested using the same images.
Abdelwahed, "Applying neural Networks
for simplified data Encryption Standards (SDES) Cipher System Cryptoanalysis," International Arab Journal of Information Technology, vol.
Meskin, "Fault detection and isolation of a dual spool gas turbine engine using dynamic neural networks
and multiple model approach," Information Sciences, vol.
Tune: Layer-by-layer performance metrics for both industry-standard and custom-designed neural networks
enable effective tuning for optimal real-world performance at ultra-low power.
These computed results are applied to train probabilistic neural networks
to predict the porosity volume.Afterwards, average porosity maps are constructed to define the lateral variations of porosity values and the prospective zones with low risk of well failure in sense of hydrocarbon production are identified.
Predicting total organic carbon removal efficiency and coagulation dosage using artificial neural networks
. Environmental Engineering Science, 29(8), 743-750.
Keywords: Robust Stability, BAM Neural Networks
, Lyapunov Theorems, Interval Matrices.