By M. M. Poulton
This booklet 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 effective clutch of ways to create a neural community software, this paintings promises quite a lot of examples of nuances in community layout, information set layout, checking out process, and blunder analysis.Computational, instead of man made, 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 man made neural community covers any implementation that's inorganic and is the main normal time period. Computational neural networks are just applied in software program yet symbolize nearly all of applications.While this booklet can't supply a blue print for each a possibility geophysics software, it does define a easy procedure that has been used effectively
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Additional info for Computational Neural Networks for Geophysical Data Processing
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. Even with normalization, the average error can be large enough that unless a very small value for the learning rate is used, the network can easily become paralyzed.
A P E that passes its computed values to an external source, an output file for example, is called an output PE. The output PEs also compute the error values for networks performing supervised learning (learning in which a desired output value is provided by the operator). Any PE that is not in an input or output layer is referred to as a hidden PE. The term hidden is used because these PEs have no direct connection to the external world. In a biological model, input PEs would be analogous to sensory neurons in our eyes, ears, nose, or skin; output PEs would be motor neurons that cause muscles to move; hidden PEs would be all the remaining neurons in the brain and nervous system that process the sensory input.
0 10000 20000 . . 30000 ~ . . . 40000 . . T. . 50000 . . . 13. The XOR problem is solved fastest by a network using a bias element connected to each hidden and output PE. 015 t- '-I-CO 001 9 ~; n," 0 . 14. The sine function is estimated with better accuracy when a bias PE is used. 7. 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.
Computational Neural Networks for Geophysical Data Processing by M. M. Poulton