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G - Prop - III : Global Optimization of Multilayer Perceptrons using anEvolutionary
This paper proposes a new version of a method (G-Prop-III, genetic backpropaga-tion) that attempts to solve the problem of nding appropriate initial weights and learning parameters for a single hidden layer Mul-tilayer Perceptron (MLP) by combining a genetic algorithm (GA) and backpropagation (BP). The GA selects the initial weights and the learning rate of the network, and changes the number o...
متن کاملcatena-Poly[[[aqua[3-(3-hydroxyphenyl)prop-2-enoato]samarium(III)]-bis[μ2-3-(3-hydroxyphenyl)prop-2-enoato]] monohydrate]
The title Sm(III) compound, {[Sm(C(9)H(7)O(3))(3)(H(2)O)]·H(2)O}(n), was obtained under hydrothermal conditions. Its structure is isotypic with the analogous Eu complex. The latter was reported incorrectly in space group P1 by Yan et al. [J. Mol. Struct. (2008), 891, 298-304]. This was corrected by Marsh [Acta Cryst. B65, 782-783] to P-1. The Sm(III) ion is nine-coordinated by O atoms from one ...
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The Polycomb group (PcG) protein Eed is implicated in regulation of imprinted X-chromosome inactivation in extraembryonic cells but not of random X inactivation in embryonic cells. The Drosophila homolog of the Eed-Ezh2 PcG protein complex achieves gene silencing through methylation of histone H3 on lysine 27 (H3-K27), which suggests a role for H3-K27 methylation in imprinted X inactivation. He...
متن کاملP-27: The Effect of Varicocelectomy onThe Serum Testosterone Level in Infertile Men with Grade (II - III) Varicocele
Background: To determine whether men with varicoceles have lower testosterone levels than those without and to ascertain if testosterone levels increase after varicocelectomy and or to determine whether the varicocele grade is related to the degree of improvement in serum testosterone levels after varicocelectomy and also older age is associated with improvements in testosterone after varicocel...
متن کاملThe Back-Prop and No-Prop Training Algorithms
Back-Prop and No-Prop, two training algorithms for multi-layer neural networks, are compared in design and performance. With Back-Prop, all layers of the network receive least squares training. With No-Prop, only the output layer receives least squares training, whereas the hidden layer weights are chosen randomly and then fixed. No-Prop is much simpler than Back-Prop. No-Prop can deliver equal...
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ژورنال
عنوان ژورنال: The Classical Review
سال: 1915
ISSN: 0009-840X,1464-3561
DOI: 10.1017/s0009840x00048289