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Split shear-wave analysis using an artificial neural networkNormal access

Authors: H. Dai and C. MacBeth
Journal name: First Break
Issue: Vol 12, No 12, December 1994 pp. 605 - 613
DOI: 10.3997/1365-2397.1994038
Language: English
Info: Article, PDF ( 1.1Mb )
Price: € 30

Artificial neural networks (ANNs) are simple models that attempt to simulate the operation of neurons in the brain. Although ANNs are relatively new in seismology, their origins can be traced back to the 1940s when psychologists began developing models of human learning. One of the most exciting developments in ANNs was the advent of the Perceptron, the idea that a network of elemental processors arrayed in a marmer reminiscent of biological neural networks might be able to learn how to recognize and classify patterns in an autonomous manner. However, in 1969, Marvin Minsky, one of the founding fathers of artificial intelligence, proved mathematically that perceptrons were incapable of solving many simpIe problems.

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