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- W2016556636 abstract "Interest in soft computing techniques, such as artificial neural networks (ANN) is growing rapidly. Feed-forward back-propagation and radial basis ANN are the most often used applications in this regard. They have been utilized to solve a number of real problems, although they gained a wide use, however the challenge remains to select the best of them in term of accuracy and efficiency performance. This paper presents a comparison between feed-forward back-propagation and radial basis ANN base on their performance. The comparison is performed using a Monte Carlo study that involves the following problems: addition, multiplication, division, powers and a production function. The result indicates that the proposed radial basis ANN results are significantly better than proposed feed-forward back-propagation ANN results for all five problems." @default.
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- W2016556636 date "2010-06-01" @default.
- W2016556636 modified "2023-09-26" @default.
- W2016556636 title "A comparison of feed-forward back-propagation and radial basis artificial neural networks: A Monte Carlo study" @default.
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- W2016556636 doi "https://doi.org/10.1109/itsim.2010.5561599" @default.
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