M. M. Poulton's Computational Neural Networks for Geophysical Data PDF

By M. M. Poulton

This ebook used to be essentially written for an viewers that has heard approximately neural networks or has had a few adventure with the algorithms, yet want to achieve a deeper knowing of the basic fabric. for those who have already got an excellent snatch of ways to create a neural community software, this paintings delivers a variety of examples of nuances in community layout, information set layout, trying out technique, and blunder analysis.Computational, instead of synthetic, modifiers are used for neural networks during this e-book to make a contrast among networks which are carried out in and those who are applied in software program. The time period man made neural community covers any implementation that's inorganic and is the main basic time period. Computational neural networks are just carried out in software program yet symbolize the majority of applications.While this ebook can't offer a blue print for each achievable geophysics software, it does define a easy process that has been used effectively

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If the error is accumulated for all training patterns prior to CHAPTER 3. MULTI-LAYER PERCEPTRONS AND B A C K - P R O P A G A T I O N LEARNING 48 changing the connection weights, we refer to the training method as batch mode. weights are updated after every pattern, we refer to the training as pattern mode. 15). The batch mode requires more computational overhead since the error terms must be stored for the entire training set. 26) are used to update the weights. When batch mode is used to update the connection weights, the cumulative error should be normalized by the number of training samples so the value that is used as error represents the average error over the training set.

The input vector Y contains one element tbr each value in the training set. 0. 6. In subsequent equations the bias connection weights will be assumed to be part of the weight vector Y and will not be shown as a separate term. At a PE in the next or hidden layer all products are summed. This is called the output of the PE. 3) Activation is a term drawn from the neurophysiology lexicon and refers to the state of a biological neuron becoming physically active if the incoming electrical stimulus exceeds the threshold of the cell.

A schedule can also be specified that allows the parameters to vary as a function of the iteration number of the training cycle. A small value for the learning rate will slow the convergence rate but will also help ensure that the global minimum will not be missed. A larger value of the learning rate is appropriate when the error surface is relatively flat. The magnitudes of the learning rate and momentum are traded off so that a small learning rate can be coupled with a larger momentum to increase convergence speed and a larger learning rate is usually coupled with a smaller momentum to help ensure stability, w ,, (t + 1) = W,, (t) + q g p, x p, + Ct( w ,, (t) - w ,, (t - 1)).

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