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."
Neural networks 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, vol.
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.
