By M.M. Poulton
This ebook used to be basically written for an viewers that has heard approximately neural networks or has had a few event with the algorithms, yet want to achieve a deeper figuring out of the basic fabric. for those who have already got an exceptional clutch of the way to create a neural community software, this paintings gives you a variety of examples of nuances in community layout, information set layout, trying out approach, and blunder analysis.Computational, instead of synthetic, modifiers are used for neural networks during this booklet to make a contrast among networks which are carried out in and those who are carried out in software program. The time period synthetic neural community covers any implementation that's inorganic and is the main normal time period. Computational neural networks are just carried out in software program yet characterize nearly all of applications.While this e-book can't supply a blue print for each attainable geophysics software, it does define a simple procedure that has been used effectively.
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Additional info for Computational Neural Networks for Geophysical Data Processing (Handbook of Geophysical Exploration: Seismic Exploration)
3b. The function generating this threshold is the logistic function that is sigmoidal in shape and usually referred to as the sigmoid function. The mathematical requirements for the threshold function are described in Chapter 3. 2 ( v - 2 4 - - 1 . . . . . . . . . 6 T 8 . . . . . . . 3a. A generalized graph of the relationship between the input current and neuron firing rate shows the "sigmoidal" shape used as the threshold function in biological neurons. 2 includes a synapse. The synapse is the location in a biological neuron that allows a signal to be transferred from one neuron to several others.
Error accumulation Weights can either be updated after each pattern presentation or after all patterns in a training set have been presented. 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.
17) Before we can adjust the weights connected to the output layer we need to know the error attributed to each PE in the hidden layer. We had the advantage for the output PEs that we knew the error based on the training set values. We do not know what the output values should be for the hidden PEs, so we need to express the relationship between the calculated values for each output PE and the activation of the PEs in the hidden layer as 2. 19) and substitute back into the equation. 20) = f [ ( S u m p k ).