By M. M. Poulton
This publication used to be essentially written for an viewers that has heard approximately neural networks or has had a few event with the algorithms, yet wish to achieve a deeper figuring out of the basic fabric. for people that have already got an excellent take hold of of the way to create a neural community software, this paintings delivers quite a lot of examples of nuances in community layout, facts set layout, checking out technique, and mistake analysis.Computational, instead of synthetic, modifiers are used for neural networks during this publication to make a contrast among networks which are carried out in and people who are applied in software program. The time period synthetic 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 signify nearly all of applications.While this ebook can't supply a blue print for each achievable geophysics program, it does define a easy technique that has been used effectively
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Extra resources for Computational Neural Networks for Geophysical Data Processing
A more detailed explanation of the mathematical underpinnings of many of the variables and their significance can be found in Bishop (1995) or Masters (1993). Networks are described by their architecture and their learning algorithm. The architecture is described by the fundamental components of PEs, connection weights and layers and the way each component interacts with the others. A connection strategy refers to the way layers and PEs within layers are connected. A feed-forward strategy means that layers are connected to other layers in one direction, from the input to the output layer.
The network must operate as a filter by learning a function that represents the noise added to the sine function. 1. Number of hidden layers Several researchers give guidance on the number of hidden layers required to solve arbitrary problems with a back-propagation-type network. The Kolmogorov's Mapping Neural Network Existence Theorem (Bishop, 1995, Hecht-Nielsen, 1990) says that a neural network with two hidden layers can solve any continuous mapping y(x) when the activation of the first hidden layer PEs is given by monotonic functions h(x~ and the activation of the second hidden layer PEs is given by CHAPTER 3.
Some researchers have suggested that the geometric mean of the inputs and outputs is a good predictor of the optimum number of hidden PEs in networks with fewer output nodes than inputs. Too few hidden PEs and the network can't make adequately complex decision boundaries. Too many and it may memorize the training set. Two excellent sources of information on understanding the role of the hidden PE are Geman et al. (1992) and Lapedes and Farber (1988). 5. RMS test error for the sine function estimation as a function of number of hidden PEs in the first and second hidden layers.