A 61-year-old man presented with an unusual PNS and absence of circulating neural auto-antibodies.
No neural auto-antibodies (anti-Purkinje cells, anti-granule cells, anti-nucleolin, anti-GABAergic synapses, DOT-BLOT IgG - Ravo) were detected, neither in serum nor in cerebro-spinal fluid (CSF).
Finite Impulse Response (FIR) with moving average are used to extract the dynamic response and frequency domain of the bridge and Neural Network Auto-Regressive (NNAR) model is used to identify the bridge movement.
Two identification models namely; Multi input single output (MISO) robust fit regression and Neural Network Auto-Regression Moving Average with eXogenous input (NNARMAX) models were used for the identification of these data.
In this text there is effected application for still image compression using two significant neural network models, auto-associative multilayer perceptron (AMLP) and self-organizing feature map (SOFM).
There are a very limited number of basic technologies, most of which can be categorized under the umbrella term "deep learning," that form the basis of almost every application out there: convolutional and recurrent neural networks, LSTM, auto-encoders, random forests, gradient boosting and a very few others.
Then the 2D mapping method based on Auto Associative Neural Networks (ANN) (particularly, the Feed Forward Bottle Neck Neural Network FFBN NNN)) was described in comparison with traditional methods.
Figure 1 Auto-associative neural networks scheme used for feature reduction.
Methods like Gaussian mixture models and auto-associative neural networks have been used for nonlinear problems.
Its performances are compared with those of recently proposed non-linear auto-associative neural networks.
Three different novelty detection algorithms are considered here: outlier analysis, density estimation and an auto-associative neural network technique.
