It can be used forspeech recognition, time series prediction, , etc.
BP is the most widely applied neural network technique.
The degree of disagreement between the neural network DIFF predictions and the other evaluations were greatest for the Upper Wye, less substantial for Kilgram, and slight for Skelton.
There are several different types of neural network.
The majority of all predictions are within 5% of the observed value although the percentage correct are in most cases less than 1% for the neural networks, and for most of the ARMA models, except for the Upper Wye ARMA model where the percentage of correct predictions is much higher.
Neural Network Prediction Of SPM Achievement - eTheses
Work in neuralnetworks has shown that it is relatively easy to implement arudimentary form of pattern completion in computers, assuming theinvariance of the input; i.e., the network should not failentirely if the input is shifted, rotated, or zoomed to someextent.
Thesis based on neural network - …
These networks comprised 14:6:1 and 14:12:1 configurations and, as before, both networks were trained to predict first FLOW and then DIFF values using the 1985 pattern sets for each individual station.
Neural Network Thesis 2013 – 265386 – ARC
Since it is not yet known how to achieve input invariancein neural networks, and because computer hardware is based on anentirely different architecture compared to the neuronal one,other computational approaches can also be of value.
Neural Network Thesis 2013 – 265386
New directions: SOM-based multi-network modellingUp to this point we have been comparing global neural network solutions with ARMA models and naive predictions all trained and validated on the same datasets.