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Titlebook: Artificial Neural Nets. Problem Solving Methods; 7th International Wo José Mira,José R. Álvarez Conference proceedings 2003 Springer-Verlag

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https://doi.org/10.1007/978-3-030-53464-6building block used for generation of Boolean functions as an example. Some comparisons with other floating gate and standard cell CMOS implementations are also done. A new FULLADDER structure containing only eight transistors is demonstrated by SPICE simulations, using a power supply voltage of 0.8
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Power Over? The Influence of Donorsicroelectrode array was inmersed in ringer solution for measurements. Resistive and capacitive components, as well as total equivalent impedances were analyzed at different current levels using Electrochemical Impedance Spectroscopy (EIS) techniques. A SPICE equivalent model was introduced. Our resu
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https://doi.org/10.1057/9781137005793n functions and batched backpropagation with different smoothing-momentum alternatives. We name this model eXtended Multi-Layer Perceptron (XMLP) because it extends the connectivity of the MLP. Here we describe its architecture, the various activation functions that it can use, its learning algorith
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Power Over? The Influence of Donorst allows synaptic changes by discrete time steps. For this purpose it is used an integrate-and-fire neuron with recurrent local connections. The connectivity of this model has been set to 24-neighbour, so there is a high degree of parallelism. After obtaining good results with the hardware implement
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https://doi.org/10.1007/978-3-658-44614-7er we introduce a notation to describe completely a rule based on this neighborhood and implement it in two ways: The first corresponds to a classical RAM-based implementation, while the second, based on concurrent cells, is useful for smaller systems in which time is a critical parameter. This impl
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Methodenkritische Reflexion und Limitation,ases. Our results show that the simplest algorithm, k-means clustering, may be the best alternative. The results of RBF are also compared with the results of Multilayer Feedforward with Backpropagation, the performance of a RBF network trained with k-means clustering is slightly better and the compu
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