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In 1960, Dr. Ledley founded the National Biomedical Research Foundation, a nonprofit organization dedicated to promoting the use of computing methods among biomedical scientists.
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The results obtained in this study can be considered as a proof of concept for the use of reservoir computing methods in the ICU.
The use of soft computing methods can certainly simplify the control on these vehicle properties.
There is a rather new tool for solving the aforesaid problem, namely the use of soft computing methods: fuzzy logic, artificial neural networks and genetic algorithms.
The use of soft computing methods to solve complicated problems in hydraulic fields is expanding (Nagy et al. 2002; Cigizoglu 2004; Giustolisi and Laucelli 2005; Alp and Cigizoglu 2007).
The SVD-based reduction gives a way for the elimination of the redundancy of these systems, and furthermore makes possible to construct several rule-bases or neural networks for the same problem with different complexity and accuracy, so wider use of soft computing methods becomes possible.
Instead, A.M.D. hopes to offer hardware manufacturers new ways to configure machines that they sell for enterprise cloud computing, a fast-growing sector of the market that uses Web computing methods for corporate needs like financial analysis and manufacturing.
As problems such as pattern recognition, system identification, and system control became difficult to solve using conventional computing methods, the concept of neural networks was inspired by the biological learning and decision-making process of the human neuron system.
Here we propose the use of a soft computing method — Gene Expression Programming (GEP) — to synthesize missing DT logs when only common logs (such as natural gamma ray — GR, or deep resistivity — REID) are present.
Authors used the soft computing method of fuzzy cognitive maps to represent and model experts knowledge of the FCM grading model.
This motivates the use of approximate methods for computing the N-body force, such as the Barnes and Hut ([1986]) tree code, as long as the approximation errors are small compared to the estimation errors.
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